Technical Program

VI111
Systems and Signals - Modeling, Identification and Signal Processing
VI111-01 Data-Driven Methods for Decisions and Control   Invited Session, 4 papers
VI111-02 Data-Driven Modeling and Learning in Dynamic Networks   Open Invited Session, 11 papers
VI111-03 Data-Driven Process Monitoring and Control for Complex Industrial Systems   Open Invited Session, 13 papers
VI111-04 Machine Learning for Monitoring and Control of Chemical and Biological Processes   Open Invited Session, 13 papers
VI111-05 Modelling, Identification and Control of Quantum Systems   Open Invited Session, 12 papers
VI111-06 Results on Nonlinear System Identification Benchmarks   Open Invited Session, 5 papers
VI111-07 Application of System Identification   Regular Session, 4 papers
VI111-08 Bayesian Methods   Regular Session, 16 papers
VI111-09 Classification, Estimation, and Filtering   Regular Session, 11 papers
VI111-10 Estimation, Identification, and Discretization of Continuous-Time Systems   Regular Session, 17 papers
VI111-11 Fault Detection and Diagnosis   Regular Session, 34 papers
VI111-12 Identification for Control   Regular Session, 9 papers
VI111-13 Linear Systems Identification   Regular Session, 7 papers
VI111-14 Learning for Modeling, Identification, and Control   Regular Session, 13 papers
VI111-15 Modeling, Identification and Control of Dynamic Networks   Regular Session, 7 papers
VI111-16 Nonlinear System Identification   Regular Session, 32 papers
VI111-17 Particle Filtering/Monte Carlo Methods   Regular Session, 5 papers
VI111-01
Data-Driven Methods for Decisions and Control Invited Session
Chair: Carè, AlgoUniversity of Brescia, Italy
Co-Chair: Garatti, SimonePolitecnico Di Milano
Organizer: Campi, MarcoUniversity of Brescia
Organizer: Carè, AlgoUniversity of Brescia, Italy
Organizer: Garatti, SimonePolitecnico Di Milano
Paper VI111-01.1 
PDF · Video · Robust Force Control for Brake-By-Wire Actuators Via Scenario Optimization (I)

Riva, GiorgioPolitecnico Di Milano
Nava, DarioPolitecnico Di Milano
Formentin, SimonePolitecnico Di Milano
Savaresi, SergioPolitecnico Di Milano
Keywords: Randomized methods
Abstract: Clamping force control in Electro Mechanical Brakes (EMBs) is a challenging task, mainly due to the nonlinear dynamics of the system and the uncertainty affecting its physical parameters. In this paper, a robust tuning of a PID control loop for an EMB is proposed. First, a control-relevant linear model of the system is derived. Then, the optimal parameters of the controller are tuned by solving a convex pole-placement problem and probabilistic robustness guarantees are provided according to the scenario theory. Finally, the performance of the proposed strategy is assessed on a complex nonlinear simulator of the EMB dynamics, and compared with the state of the art approach for robust control of EMBs.
Paper VI111-01.2 
PDF · Video · Data-Driven Control of Unknown Systems: A Linear Programming Approach (I)

Tanzanakis, AlexandrosETH Zurich
Lygeros, JohnETH Zurich
Keywords: Learning for control
Abstract: We consider the problem of discounted optimal state-feedback regulation for general unknown deterministic discrete-time systems. It is well known that open-loop instability of systems, non-quadratic cost functions and complex nonlinear dynamics, as well as the on-policy behavior of many reinforcement learning (RL) algorithms, make the design of model-free optimal adaptive controllers a challenging task. We depart from commonly used least-squares and neural network approximation methods in conventional model-free control theory, and propose a novel family of data-driven optimization algorithms based on linear programming, off-policy Q-learning and randomized experience replay. We develop both policy iteration (PI) and value iteration (VI) methods to compute an approximate optimal feedback controller with high precision and without the knowledge of a system model and stage cost function. Simulation studies confirm the effectiveness of the proposed methods.
Paper VI111-01.3 
PDF · Video · No-Regret Learning from Partially Observed Data in Repeated Auctions (I)

Karaca, OrcunETH Zurich
Sessa, Pier GiuseppeETH Zurich
Leidi, AnnaETH Zurich
Kamgarpour, MaryamSwiss Federal Institute of Technology
Keywords: Multi-agent systems, Stochastic control and game theory, Learning for control
Abstract: We study a general class of repeated auctions, such as the ones found in electricity markets, as multi-agent games between the bidders. In such a repeated setting, bidders can adapt their strategies online using no-regret algorithms based on the data observed in the previous auction rounds. Well-studied no-regret algorithms depend on the feedback information available at every round, and can be mainly distinguished as bandit (or payoff-based), and full-information. However, the information structure found in auctions lies in between these two models, since participants can often obtain partial observations of their utilities under different strategies. To this end, we modify existing bandit algorithms to exploit such additional information. Specifically, we utilize the feedback information that bidders can obtain when their bids are not accepted, and build a more accurate estimator of the utility vector. This results in improved regret guarantees compared to standard bandit algorithms. Moreover, we propose a heuristic method for auction settings where the proposed algorithm is not directly applicable. Finally, we demonstrate our findings on case studies based on realistic electricity market models.
Paper VI111-01.4 
PDF · Video · A Scenario-Based Approach to Multi-Agent Optimization with Distributed Information (I)

Falsone, AlessandroPolitecnico Di Milano
Margellos, KostasUniversity of Oxford
Prandini, MariaPolitecnico Di Milano
Garatti, SimonePolitecnico Di Milano
Keywords: Multi-agent systems, Randomized methods, Learning for control
Abstract: In this paper, we consider optimization problems involving multiple agents. Each agent introduces its own constraints on the optimization vector, and the constraints of all agents depend on a common source of uncertainty. We suppose that uncertainty is known locally to each agent through a private set of data (multi-agent scenarios), and that each agent enforces its scenario-based constraints to the solution of the multi-agent optimization problem. Our goal is to assess the feasibility properties of the corresponding multi-agent scenario solution. In particular, we are able to provide a priori certificates that the solution is feasible for a new occurrence of the global uncertainty with a probability that depends on the size of the datasets and the desired confidence level. The recently introduced wait-and-judge approach to scenario optimization and the notion of support rank are used for this purpose. Notably, decision-coupled and constrained-coupled uncertain optimization programs for multi-agent systems fit our framework and, hence, any distributed optimization scheme to solve the associated multi-agent scenario problem can be accompanied with our a priori probabilistic feasibility certificates.
VI111-02
Data-Driven Modeling and Learning in Dynamic Networks Open Invited Session
Chair: Van den Hof, Paul M.J.Eindhoven University of Technology
Co-Chair: Rantzer, AndersLund Univ
Organizer: Van den Hof, Paul M.J.Eindhoven University of Technology
Organizer: Chiuso, AlessandroUniversity of Padova
Organizer: Goncalves, Jorge M.University of Luxembourg
Paper VI111-02.1 
PDF · Video · Graph Theoretic Foundations of Cyclic and Acyclic Linear Dynamic Networks (I)

Johnson, CharlesBrigham Young University
Woodbury, NathanBrigham Young University
Warnick, SeanBrigham Young Univ
Keywords: Dynamic Networks
Abstract: Dynamic Networks are signal flow graphs explicitly partitioning structural information from dynamic or behavioral information in a dynamic system. This paper develops the mathematical foundations underlying this class of models, revealing structural roots for system concepts such as system behavior, well-posedness, causality, controllability, observability, minimality, abstraction, and realization. This theory of abstractions uses graph theory to systematically and rigorously relate LTI state space theory, developed by Kalman and emphasizing differential equations and linear algebra, to the operator theory of Weiner, emphasizing complex analysis, and Willem’s behavioral theory. New systems concepts, such as net effect, complete abstraction, and extraneous realization, are introduced, and we reveal conditions when acyclic abstractions exist for a given network, opening questions about their use in network reconstruction and other applications.
Paper VI111-02.2 
PDF · Video · Recursive Estimation of Three Phase Line Admittance in Electric Power Networks (I)

Mishra, AdityaUniversity of California San Diego
de Callafon, RaymondUniversity of California, San Diego
Keywords: Dynamic Networks, Machine learning, Recursive identification
Abstract: Synchronized phasor measurements in power transmission and distribution networks enable real-time monitoring of voltage and currents. Such measurements can be used to monitor power flow, but also to monitor important electric parameters of the network. In this paper, it is shown how synchrophasor measurements can be used for real-time monitoring of the admittance of the connections between buses in a power network, typically the three-phase transmission or distribution lines. The objective is to formulate admittance monitoring capabilities in which changes in three-phase line admittance can be monitored in real-time and achieved by the formulation of synchrophasor-based recursive estimation techniques over short time intervals.
Paper VI111-02.3 
PDF · Video · Excitation Allocation for Generic Identifiability of a Single Module in Dynamic Networks: A Graphic Approach (I)

Shi, ShenglingEindhoven University of Technology
Cheng, XiaodongEindhoven University of Technology
Van den Hof, Paul M.J.Eindhoven University of Technology
Keywords: Dynamic Networks, Identifiability, Input and excitation design
Abstract: For identifiability of a single module in a dynamic network, excitation signals need to be allocated at particular nodes in the network. Current techniques provide analysis tools for verifying identifiability in a given situation, but hardly address the synthesis question: where to allocate the excitation signals to achieve generic identifiability. Starting from the graph topology of the considered network model set, a new analytic result for generic identifiability of a single module is derived based on the concept of disconnecting sets. For the situation that all node signals are measured, the vertices in a particular disconnecting set provide the potential locations to allocate the excitation signals. Synthesis approaches are then developed to allocate excitation signals to guarantee generic identifiability.
Paper VI111-02.4 
PDF · Video · Consistent Identification of Dynamic Networks Subject to White Noise Using Weighted Null-Space Fitting (I)

Fonken, StefanieTUe
Ferizbegovic, MinaKTH
Hjalmarsson, HåkanKTH
Keywords: Dynamic Networks
Abstract: Identification of dynamic networks has been a flourishing area in recent years. However, there are few contributions addressing the problem of simultaneously identifying all modules in a network of given structure. In principle the prediction error method can handle such problems but this methods suffers from well known issues with local minima and how to find initial parameter values. Weighted Null-Space Fitting is a multi-step least-squares method and in this contribution we extend this method to rational linear dynamic networks of arbitrary topology with modules subject to white noise disturbances. We show that WNSF reaches the performance of PEM initialized at the true parameter values for a fairly complex network, suggesting consistency and asymptotic efficiency of the proposed method.
Paper VI111-02.5 
PDF · Video · Single Module Identification in Dynamic Networks - the Current Status (I)

Van den Hof, Paul M.J.Eindhoven University of Technology
Ramaswamy, Karthik R.Eindhoven University of Technology
Keywords: Dynamic Networks, Identifiability, Closed loop identification
Abstract: Over the last decade, the problem of data-driven modeling in linear dynamic networks has been introduced in the literature, and has shown to contain many different challenging research questions, that go far beyond the classical problems in open-loop and closed-loop identification. The structural and topological properties of networks become a central ingredient in the related identification setting, as well as the selection of locations for signals to be sensed and for excitation signals to be added. In this seminar we will present an overview of recent results that are obtained for the problem of identification of a single link/module in a dynamic network of which the topology is given. The surveyed methods include extensions of the direct and indirect methods of closed-loop identification, as well as Wiener filter approaches and Bayesian kernel-based methods. Particular attention will be given to the selection of signals that need to be available for measurement/excitation, and accuracy properties of the estimated models in terms of consistency and minimum variance properties.
Paper VI111-02.6 
PDF · Video · Data-Driven Distributed Algorithms for Estimating Eigenvalues and Eigenvectors of Interconnected Dynamical Systems (I)

Gusrialdi, AzwirmanTampere University
Qu, ZhihuaUniversity of Central Florida
Keywords: Distributed control and estimation, Learning for control
Abstract: The paper presents data-driven algorithms to estimate in a distributed manner the eigenvalues, right and left eigenvectors of an unknown linear (or linearized) interconnected dynamic system. In particular, the proposed algorithms do not require the identification of the system model in advance before performing the estimation. As a first step, we consider interconnected dynamical system with distinct eigenvalues. The proposed strategy first estimates the eigenvalues using the well-known Prony method. The right and left eigenvectors are then estimated by solving distributively a set of linear equations. One important feature of the proposed algorithms is that the topology of communication network used to perform the distributed estimation can be chosen arbitrarily, given that it is connected, and is also independent of the structure or sparsity of the system (state) matrix. The proposed distributed algorithms are demonstrated via a numerical example.
Paper VI111-02.7 
PDF · Video · Minimax Adaptive Control for State Matrix with Unknown Sign (I)

Rantzer, AndersLund Univ
Keywords: Learning for control
Abstract: For linear time-invariant systems having a state matrix with uncertain sign, we formulate and solve a minimax adaptive control problem as a zero sum dynamic game. Explicit expressions for the optimal value function and the optimal control law are given in terms of a Riccati equation. The optimal control law is adaptive in the sense that past data is used to estimate the uncertain sign for prediction of future dynamics. Once the sign has been estimated, the controller behaves like standard H-infinity optimal state feedback.
Paper VI111-02.8 
PDF · Video · Inferring Individual Network Edges - with Application to Target Identification in Gene Networks (I)

Wang, YuKTH Royal Institute of Technology
Jacobsen, EllingKTH Royal Institute of Technology
Keywords: Dynamic Networks
Abstract: The paper considers the problem of inferring individual network edges from time-series data. This is the problem faced in target identification, but also important in cases where it is of interest to learn whether two specific network nodes interact directly as well as in cases where there is insufficient information to infer the full network. The proposed inference method is based on taking a geometric perspective on a corresponding regression problem. We show that, by considering the span of individual node response vectors in sample space, it is possible to identify a given edge with a label of confidence even if the available data are not informative to infer other parts of the network. Furthermore, the method points to what further experiments are needed to infer edges for which the available response data are not sufficiently informative. We demonstrate the results on a target identification problem of a nonlinear 20-gene network and show that targets can be identified independently from a single time-series experiment using significantly fewer samples than the number of nodes in the network.
Paper VI111-02.9 
PDF · Video · Data-Driven Verification under Signal Temporal Logic Constraints (I)

Salamati, AliLudwig Maximilian University of Munich
Soudjani, SadeghNewcastle University
Zamani, MajidUniversity of Colorado Boulder
Keywords: Bayesian methods, Experiment design, Grey box modelling
Abstract: We consider systems under uncertainty whose dynamics are partially unknown. Our aim is to study satisfaction of temporal properties by trajectories of such systems. We express these properties as signal temporal logic formulas and check if the probability of satisfying the property is at least a given threshold. Since the dynamics are parameterized and partially unknown, we collect data from the system and employ Bayesian inference techniques to associate a confidence value to the satisfaction of the property. The main novelty of our approach is to combine both data-driven and model-based techniques in order to have a two-layer probabilistic reasoning over the behavior of the system: one layer is related to the stochastic noise inside the system and the next layer is related to the noisy data collected from the system. We provide approximate algorithms for computing the confidence for linear dynamical systems.
Paper VI111-02.10 
PDF · Video · Learning Sparse Linear Dynamic Networks in a Hyper-Parameter Free Setting (I)

Venkitaraman, ArunKTH Royal Institute of Technology
Hjalmarsson, HåkanKTH
Wahlberg, BoKTH Royal Institute of Technology
Keywords: Dynamic Networks
Abstract: We address the issue of estimating the topology and dynamics of sparse linear dynamic networks in a hyperparameter-free setting. We propose a method to estimate the network dynamics in a computationally efficient and parameter tuning-free iterative framework known as SPICE (Sparse Iterative Covariance Estimation). Our approach does not assume that the network is undirected and is applicable even with varying noise levels across the modules of the network. We also do not assume any explicit prior knowledge on the network dynamics. Numerical experiments with realistic dynamic networks illustrate the usefulness of our method.
Paper VI111-02.11 
PDF · Video · A Motif-Based Approach to Processes on Networks: Process Motifs for the Differential Entropy of the Ornstein-Uhlenbeck Process (I)

Schwarze, AliceUniversity of Washington
Wray, JonnyE-Therapeutics
Porter, Mason A.University of California Los Angeles
Keywords: Dynamic Networks, Time series modelling, Closed loop identification
Abstract: A challenge in neuroscience and many other fields of research is the inference of a network's structure from observations of dynamics on the network. Understanding the relationship between network structure and dynamics on a network can help improve methods for network inference. We consider ``process motifs'' on a network as building blocks of processes on networks and propose to distinguish process motifs and graphlets as two different types of network motifs. We demonstrate that the analysis of process motifs can yield insights into the mechanisms by which processes and network structure contribute to differential entropy and other information-based properties of stochastic processes on networks, and we discuss the relationship between process motifs and graphlets.
VI111-03
Data-Driven Process Monitoring and Control for Complex Industrial Systems Open Invited Session
Chair: Shardt, Yuri A.W.Technical University of Ilmenau
Co-Chair: Yang, XuUniversity of Science and Technology Beijing
Organizer: Shardt, Yuri A.W.Technical University of Ilmenau
Organizer: Brooks, KevinBluESP
Organizer: Yang, XuUniversity of Science and Technology Beijing
Organizer: Torgashov, AndreiInstitute for Automation and Control Processes FEB RAS
Paper VI111-03.1 
PDF · Video · Soft Sensor Design for Restricted Variable Sampling Time (I)

Griesing-Scheiwe, FritjofTU Chemnitz
Shardt, Yuri A.W.Technical University of Ilmenau
Pérez Zuñiga, GustavoPontifical Catholic University of Peru
Yang, XuUniversity of Science and Technology Beijing
Keywords: Frequency domain identification, Subspace methods, Closed loop identification
Abstract: Difficult-to-obtain variables in industrial applications have led to the rise of soft sensors, which use prior system information and measurements to estimate these difficult-to-obtain variables. In real systems, the measurements that need to be estimated by a soft sensor are often infrequently measured or delayed. Sometimes, these delays and sampling time are variable in time. Though there are papers considering soft sensors in the presence of time delays and different sampling times, the variation of those parameters has not been considered when evaluating the adequacy of the soft sensors. Therefore, this paper will evaluate the impact of such variations for a data-driven soft sensor and propose modifications of the soft sensor that increase its robustness. The reliability of its estimate will be shown using the Bauer-Premaratne-Durán Theorem. Furthermore, the soft sensor will be simulated applying it to a continuous stirred tank reactor. Simulation showed that the modified soft sensor gives good estimates, whereas the traditional soft sensor gives an unstable estimate.
Paper VI111-03.2 
PDF · Video · Sensor Fault Detection for Salient PMSM Based on Parity-Space Residual Generation and Robust Exact Differentiation (I)

Jahn, BenjaminNidec driveXpert GmbH / TU Ilmenau
Brückner, MichaelNidec driveXpert GmbH
Gerber, StanislavNidec driveXpert GmbH
Shardt, Yuri A.W.Technical University of Ilmenau
Keywords: Fault detection and diagnosis, Nonlinear system identification, Filtering and smoothing
Abstract: An online model-based fault detection and isolation method for salient permanent magnet synchronous motors is proposed using the parity-space approach. Given the polynomial model equations, Buchberger’s algorithm is used to eliminate the unknown variables (e.g. states, unmeasured inputs) resulting in analytic redundancy relations for residual generation. Furthermore, in order to obtain the derivatives of measured signals needed by such a residual generator, robust exact differentiators are used. The fault detection and isolation method is demonstrated using simulation of various fault scenarios for a speed controlled salient motor showing the effectiveness of the presented approach.
Paper VI111-03.3 
PDF · Video · Mechatronics Applications of Condition Monitoring Using a Statistical Change Detection Method (I)

Mazzoleni, MirkoUniversity of Bergamo
Scandella, MatteoUniversity of Bergamo
Maurelli, LucaUniversity of Bergamo
Previdi, FabioUniversita' Degli Studi Di Bergamo
Keywords: Fault detection and diagnosis, Nonparametric methods, Machine learning
Abstract: In this paper, we propose the use of a change detection method to perform condition monitoring of mechanical components. The aim is to look for statistical changes in the distribution of features extracted from raw measurements, such as Root Mean Square or Crest Factor indicators. The proposed method works in a batch fashion, comparing data from one experiment to another. When these distributions differ by a specified amount, a degradation score is increased. The approach is tested on three experimental industrial applications: (i) an Electro-Mechanical Actuator (EMA) employed in flight applications, where the focus of the monitoring is on the ballscrew transmission; (ii) a CNC workbench, where the focus is on the vertical axe bearing, (iii) an industrial EMA with focus on the ballscrew bearing. All components undergone a severe experimental degradation process, that ultimately led to their failure. Results show how the proposed method is able to assess components degradation prior to their failure.
Paper VI111-03.4 
PDF · Video · Data-Driven Model Predictive Monitoring for Dynamic Processes (I)

Jiang, QingchaoEast China University of Science and Technology
Yi, HuaikuanEast China University of Science and Technology
Yan, XuefengKey Laboratory of Advanced Control and Optimization ForChemical
Zhang, XinminKyoto University
Huang, JianUniversity of Science and Technology Beijing
Keywords: Fault detection and diagnosis
Abstract: Process monitoring plays an important role in maintaining favorable process operation conditions and is gaining increasing attention in both academic community and industrial applications. This paper proposes a data-driven model predictive fault detection method to achieve efficient monitoring of dynamic processes. First, a measurement sample is projected into a dominant latent variable subspace that captures main variance of the process data and a residual subspace. Then the dominant latent variable subspace is further decomposed as a dynamic feature subspace and a static feature subspace. A fault detection residual is generated in each subspace, and corresponding monitoring statistic is established. By using the model predictive monitoring scheme, not only the status of a process but also the type of a detected fault, namely a dynamic feature fault or a static feature fault, can be identified. Effectiveness of the proposed data-driven model predictive monitoring scheme is tested on a lab-scale distillation process.
Paper VI111-03.5 
PDF · Video · Data Quality Assessment for System Identification in the Age of Big Data and Industry 4.0 (I)

Shardt, Yuri A.W.Technical University of Ilmenau
Yang, XuUniversity of Science and Technology Beijing
Brooks, KevinBluESP
Torgashov, AndreiInstitute for Automation and Control Processes FEB RAS
Keywords: Frequency domain identification, Identifiability, Closed loop identification
Abstract: As the amount of data stored from industrial processes increases with the demands of Industry 4.0, there is an increasing interest in finding uses for the stored data. However, before the data can be used its quality must be determined and appropriate regions extracted. Initially, such testing was done manually using graphs or basic rules, such as the value of a variable. With large data sets, such an approach will not work, since the amount of data to tested and the number of potential rules is too large. Therefore, there is a need for automated segmentation of the data set into different components. Such an approach has recently been proposed and tested using various types of industrial data. Although the industrial results are promising, there still remain many unanswered questions including how to handle a priori knowledge, over- or undersegmentation of the data set, and setting the appropriate thresholds for a given application. Solving these problems will provide a robust and reliable method for determining the data quality of a given data set.
Paper VI111-03.6 
PDF · Video · An Optimal Distributed Fault Detection Scheme for Large-Scale Systems with Deterministic Disturbances (I)

Zhang, JiaruiUniversity of Duisburg-Essen
Li, LinlinUniversity of Duisburg Essen
Keywords: Fault detection and diagnosis, Distributed control and estimation
Abstract: The main objective of this paper is to develop an optimal distributed fault detection (FD) approach for large-scale systems in the presence of unknown deterministic disturbances using the measurement of sensor networks. To be specific, the design approach consists of two phases: the distributed offline training phase and the online implementation phase. The offline training phase includes distributed iterative learning and average consensus algorithm. It is worth mentioning that, the distributed approach avoids enormous computational costs and complex information exchanges. Finally, a numerical example is illustrated to show that the distributed approach can successfully and efficiently accomplish the FD task.
Paper VI111-03.7 
PDF · Video · Multimode Process Monitoring and Fault Diagnosis Based on Tensor Decomposition (I)

Zhao, ShanshanUniversity of Science and Technology Beijing
Zhang, KaiUniversity of Duisburg-Essen
Peng, KaixiangUniv of Science and Technology, Beijing, China
Zhang, ChuanfangUniversity of Science and Technology Beijing
Yang, XuUniversity of Science and Technology Beijing
Keywords: Fault detection and diagnosis, Subspace methods
Abstract: Nowadays, many industrial processes generate large amounts of multimode data,which generally have a natural tensor structure, causing some faults invisible with traditional process monitoring (PM) and fault diagnosis (FD) methods. Tensor decomposition (TD) is a more practical approach for its effectiveness in solving high dimensionality problems as well as indicating the links between different modes. This paper proposes a common and individual feature extraction method based on TD, which identifies and separates the common and individual features from multimode data. The newly proposed approach is applied to a typical multimode hot strip mill process (HSMP), where common and individual feature for all steel products are existing. The final results indicate that the proposed approach can accurately detect and identify different faults in the HSMP.
Paper VI111-03.8 
PDF · Video · A Study of Complex Industrial Systems Based on Revised Kernel Principal Component Regression Method (I)

Chengyuan, SunNortheastern University
Ma, HongJunNortheastern University
Keywords: Fault detection and diagnosis, Nonlinear system identification, Identification for control
Abstract: As a data-driven process monitoring method, multivariable statistics techniques have special potentials and advantages to handle the increasingly prominent "Big data challenge" in the complex industrial systems. However, the standard partial least square (PLS) method and the principal component regression (PCR) method cannot maintain stable function in the nonlinear operating environment. In order to capture the precise relation of process variables and product variables, an approach called the revised kernel PCR (RKPCR) method is proposed in this thesis to resolve the problems encountered in the traditional PCR method. In addition, a brief and effective diagnosis logic is designed to decrease the difficulty of fault diagnosis. Finally, the effectiveness of the RKPCR algorithm is illustrated utilizing the Tennessee Eastman case (TEC) platform.
Paper VI111-03.9 
PDF · Video · Data Selection Methods for Soft Sensor Design Based on Feature Extraction (I)

Caponetto, RiccardoUniv of Catania
Graziani, SalvatoreUniversity of Catania
Xibilia, M. GabriellaUniversita' Degli Studi Di Messina
Keywords: Nonlinear system identification, Machine learning
Abstract: Data selection is a critical issue in data-driven soft sensor design. The paper proposes a new method for data selection based on a feature extraction step, followed by data selection algorithms. The method has been applied to an industrial case study, i.e., the estimation of the quality of processed wastewater produced by a Sour Water Stripping plant working in a refinery. The paper reports the results obtained with different data selection algorithms. The comparison has been performed both by using raw data and the feature extraction phase.
Paper VI111-03.10 
PDF · Video · Fault Detection in Shipboard Integrated Electric Propulsion System with EEMD and XGBoost (I)

Liu, ShengHarbin Engineering University
Sun, YueHarbin Engineering University
Zhang, LanyongHarbin Engineering University
Keywords: Fault detection and diagnosis, Machine learning
Abstract: In this paper, a fault detection method of shipboard medium-voltage DC (MVDC) integrated electric propulsion system (IEPS) based on Ensemble Empirical Mode Decomposition (EEMD) and XGBoost is proposed. Particle swarm optimization (PSO) is used to optimize the parameters to solve the problem that the standard deviation of auxiliary white noise in EEMD needs to be artificially selected. Firstly, the voltage signal on the DC bus is preprocessed by PSO-EEMD, which is decomposed into a set of Intrinsic Mode Functions (IMFs) according to the local characteristic time scale of the signal, and then the energy entropy is calculated as the fault feature vector. The fault feature vector is used to train and test the fault classifier based on XGBoost, and finally the fault detection is completed. The simplified model of shipboard MVDC IEPS is built in AppSIM Time Simulator. The faults on generator output and DC cable are used to verify the proposed fault detection method. Fault feature extraction method and fault classifier design are completed in Python. Verification by simulation platform and comparison with other intelligent detection methods, it is proved that proposed detection method can detect different faults quickly and accurately, is enabled for future practical use.
Paper VI111-03.11 
PDF · Video · Feature Based Causality Analysis and Its Applications in Soft Sensor Modeling (I)

Yu, FengTsinghua University
Cao, LiangUniversity of British Columbia
Li, WeiyangTsinghua University
Yang, FanTsinghua University
Shang, ChaoTsinghua University
Keywords: Time series modelling, Grey box modelling
Abstract: In industrial processes, causality analysis plays an important role in fault detection and topology building. Aiming to attenuate the influence of common correlation and noise, a feature based causality analysis method is proposed. By using the orthogonality and de-noising in feature analysis, it can capture more efficient causal factors. Moreover, better causal factors can make better predictions. Soft sensors based on least-squares regression and two neural networks are tested to compare the performance when using different causal factors and not using causal factors. The results show that the causal feature based soft sensors obtain the best performance and causal factors are crucial to prediction performance. Hence, it has great application potential owing to its strong interpretability and good accuracy.
Paper VI111-03.12 
PDF · Video · Optimal Estimation of Gasoline LP-EGR Via Unscented Kalman Filtering with Mixed Physics-based/Data-Driven Components Modeling (I)

Kim, KwangminSeoul National University
Kim, JinsungHyundai Motor Company
Kwon, OheunHyundai Motor Company
Oh, Se-KyuHyundai Motor Company
Kim, Yong-WhaHyundai Motor Company
Lee, DongjunSeoul National University
Keywords: Estimation and filtering, Machine learning, Mechanical and aerospace estimation
Abstract: We propose a novel optimal estimation methodology for gasoline engine LP (low-pressure) EGR (exhaust gas recirculation) air-path system, which allows us to implement virtual sensors for oxygen mass fraction at the intake manifold and EGR mass flow rate at the LP-EGR valve, real sensors for them too expensive to deploy in production cars. We first decompose the LP-EGR air-path system into several sub-components; and opportunistically utilize physics-based modeling or data-driven modeling for each component depending on their model complexity. In particular, we apply the technique of MLP (multi-layer perceptron) as a means for data-driven modeling of LP-EGR/throttle valves and engine cylinder valve aspiration dynamics, all of which defy accurate physics-based modeling, that is also simple enough for real-time running. We further optimally combine these physics-based and data-driven modelings in the framework of UKF (unscented Kalman filtering), and also manifest via formal analysis that this mixed physics-based/data-driven modeling renders our estimator much faster to run as compared to the case of full data-driven MLP modeling. In doing so, we also extend the standard UKF theory to the more general case, where the system contains non-additive uncertainties both in the measurement and process models with cross-correlations and state-dependent variances, which stems from the inherent peculiar structure of our mixed physics-based/data-driven modeling approach, for the UKF formulation. Experiments are also performed to show the theory.
Paper VI111-03.13 
PDF · Video · A Data-Driven Predictive Control Structure in the Behavioral Framework (I)

Wei, LaiUniversity of New South Wales
Yan, YitaoUniversity of New South Wales
Bao, JieThe University of New South Wales
Keywords: Machine learning, Learning for control
Abstract: This paper presents a data-driven predictive control (DPC) algorithm for linear time-invariant (LTI) systems in the behavioral framework. The system is described by the parametrization of the Hankel matrix constructed from its measured trajectories. The proposed structure follows a two-step procedure. The existence of a controlled behavior is firstly verified from the perspective of dissipativity with the aid of quadratic difference forms (QdFs), then the controlled trajectory is selected from the original uncontrolled behavior through optimization. An illustrative example is presented to demonstrate the effectiveness of the proposed approach.
VI111-04
Machine Learning for Monitoring and Control of Chemical and Biological
Processes
Open Invited Session
Chair: Tulsyan, AdityaMassachusetts Institute of Technology
Co-Chair: Lee, Jong MinSeoul National University
Organizer: Gopaluni, BhushanUniversity of British Columbia
Organizer: Tulsyan, AdityaMassachusetts Institute of Technology
Organizer: Chachuat, BenoitImperial College London
Organizer: Chiang, LeoThe Dow Chemical Company
Organizer: Huang, BiaoUniv. of Alberta
Organizer: Lee, Jong MinSeoul National University
Paper VI111-04.1 
PDF · Video · Developing a Deep Learning Estimator to Learn Nonlinear Dynamic Systems (I)

Wang, KaiCentral South University
Chen, JunghuiChung-Yuan Christian Univ
Wang, YalinCentral South University
Keywords: Nonlinear system identification, Machine learning, Estimation and filtering
Abstract: Process complexities are characterized by strong nonlinearities, dynamics and uncertainties. Modeling such a complex process requires a flexible model with deep layers describing the corresponding strong nonlinear dynamic behavior. The proposed model is constructed by deep neural networks to represent the process of state transition and observation generation, both of which together constitute a stochastic nonlinear state space model. This model is evolved from the variational auto-encoder learned by the stochastic expectation-maximization algorithm. To solve the complexity of posteriors for dynamic processes, the posterior distributions with respect to state variables are constructed by a forward-backward recurrent neural network. One example is given to validate that the proposed method outperforms the comparative methods in modeling complex nonlinearities.
Paper VI111-04.2 
PDF · Video · Fault Detection for Geological Drilling Processes Using Multivariate Generalized Gaussian Distribution and Kullback Leibler Divergence (I)

Li, YupengChina University of Geoscience
Cao, WeihuaChina University of Geosciences
Hu, WenkaiChina University of Geosciences
Gan, ChaoChina University of Geosciences
Wu, MinChina University of Geosciences
Keywords: Fault detection and diagnosis, Machine learning, Time series modelling
Abstract: The presence of downhole faults compromises the safety and also leads to increased maintenance costs in complex geological drilling processes. In order to achieve timely and accurate detection of downhole faults, a systematic fault detection method is proposed based on the Multivariate Generalized Gaussian Distribution (MGGD) and the Kullback Leibler Divergence (KLD). Uncorrelated components are obtained from the original drilling process signals using the principle component analysis; then, the distribution of components is estimated using the MGGD; afterwards, the KLD is calculated based on a deduced analytic formula; last, the downhole faut is detected by comparing the calculated KLD with the alarm threshold obtained from normal data. The effectiveness and practicality of the proposed method are demonstrated by application to a real drilling process.
Paper VI111-04.3 
PDF · Video · Condition-Based Sensor-Health Monitoring and Maintenance in Biomanufacturing (I)

Tulsyan, AdityaMassachusetts Institute of Technology
Garvin, ChristopherAmgen Inc
Undey, CenkAmgen Inc
Keywords: Machine learning, Fault detection and diagnosis
Abstract: In the Biotechnology 4.0 paradigm, process analytical technology (PAT) tools are being increasingly deployed in biomanufacturing to gain improved process insights through extensive use of advanced and automated sensing techniques. Critical parameters, such as pH, dissolved oxygen (DO), temperature and metabolite concentrations, are routinely measured and controlled in a cell culture process. While these extensive networks of sensors generate critical process information and insights, they are also prone to failures and malfunctions. In this paper, we propose a condition-based maintenance (CbM) framework for real-time sensor-health management, with a focus on fault detection, diagnosis, and prognostics. To this effect, a slow-feature analysis (SFA)-based platform is proposed for the detection and diagnosis of sensor- health. For health prognostics, a Gaussian process (GP) model is proposed for forecasting the remaining useful life (RUL) of the sensor along with the probability of failure. The efficacy of the proposed sensor-heath management strategy is demonstrated in a biomanufacturing process.
Paper VI111-04.4 
PDF · Video · A Hybrid Modeling Method Based on Neural Networks and Its Application to Microwave Filter Tuning (I)

Bi, LeyuChina University of Geosciences, Wuhan
Cao, WeihuaChina University of Geosciences
Hu, WenkaiChina University of Geosciences
Yuan, YanChina University of Geosciences
Wu, MinChina University of Geosciences
Keywords: Machine learning, Hybrid and switched systems modeling, Mechanical and aerospace estimation
Abstract: In performance tuning of many electromechanical devices, well-trained operators are in great demand. However, manual tuning is costly and time-consuming, and thus do not conform to the trend of smart manufacturing. Microwave filters are typical electromechanical devices. Their tuning performance is limited by low extraction accuracy and high dimensionality of circuit features. In this paper, a hybrid modeling method based on neural networks is proposed to get better tuning performance. First, a curve-shape-based modeling method using Convolutional Neural Networks is presented to bypass the cumbersome extraction of circuit features. Second, an multi-model optimized fusion model based on Elman Neural Networks is constructed to cope with the high-dimensional property of circuit features, and further improve modeling accuracy. The effectiveness of the hybrid modeling method is demonstrated through experiments. It achieves better tuning performance with fewer samples compared with two single modeling methods.
Paper VI111-04.5 
PDF · Video · Robust Interval Prediction Model Identification with a Posteriori Reliability Guarantee (I)

Wang, ChaoTsinghua University
Shang, ChaoTsinghua University
Yang, FanTsinghua University
Huang, DexianTsinghua University
Yu, BinHengli Petrochemical Co., Ltd
Keywords: Stochastic system identification, Randomized methods, Nonlinear system identification
Abstract: In classical paradigm of model identification, a single prediction value is returned as a point estimate of the output. Recently, the interval prediction model (IPM) has been receiving increasing attentions. Different from generic models, an IPM gives an interval of confidence as the prediction that covers the majority of training data while being as tight as possible. However, due to the randomness of sampling training data, the reliability of IPM constructed is uncertain. In this paper, we focus on a general class of IPMs where a fraction of data samples can be discarded to pursue robustness, and establish an appropriate a posteriori reliability guarantee. It relies on counting the "decisive" constraints associated with the optimal solution, and generally leads to reduced conservatism and better estimation performance than the existing performance bounds. Moreover, the guarantee holds irrespective of the data generation mechanism, which informs the decision maker of the prediction confidence in the absence of precise knowledge about data distribution. Its effectiveness is illustrated based on numerical examples.
Paper VI111-04.6 
PDF · Video · Wave Propagation Patterns in Gas Pipelines for Fault Location (I)

Peralta, JesúsInstituto De Ingeniería, UNAM
Verde, CristinaInst. De Ingenieria, UNAM
Delgado, FerminUniversidad Nacional Autónoma De México
Keywords: Fault detection and diagnosis, Time series modelling, Frequency domain identification
Abstract: Based on the reflectometry phenomenon and the behavior of an acoustic signal in a gas pipeline, this work proposes a fault location test for pipelines, which is formally justified for an infinite-dimension model of acoustic wave propagation in a closed conduit with viscous absorption. The test consists of disturbing the medium by an acoustic pulse at one extreme of the pipeline and of registering the transient response at an observation point. In this way, the waveform of the transient response of the pressure allows distinguishing the pattern of a healthy system from a pipeline with diverse faults and to allow locating the position of the damage.
Paper VI111-04.7 
PDF · Video · Multirate Fusion of Data Sources with Different Quality (I)

Sansana, JoelUniversity of Coimbra
Rendall, RicardoDow
Wang, ZhenyuTufts University
Chiang, LeoThe Dow Chemical Company
Seabra dos Reis, Marco P.University of Coimbra
Keywords: Filtering and smoothing, Machine learning, Bayesian methods
Abstract: The chemical process industry makes increasingly use of a diversity of data collectors, that should be properly integrated to build effective solutions for process monitoring, control and optimization. Concerning the assessment of products properties, one of the most common scenarios involve the collection of data from plant laboratories that provide more accurate measurements at lower rates, together with more frequent measurements or predictions of lower quality. Soft sensors and online analyzers are examples of viable alternatives for acquiring more frequent and updated information, although with a higher uncertainty. All of these data collectors have informative value and should be considered when it comes to estimate key product attributes. This is the goal of fusion methods, whose importance grows together with the increase in the number of sensors and data sources available. In this article, two fusion schemes that address prevailing characteristics of industrial data are proposed and compared: one version of the classic tracked Bayesian fusion scheme (TBF) and a novel modification of the track-to-track algorithm, designated as bias-corrected track-to-track fusion (BCTTF). The proposed methodologies are able to cope with the multirate nature of data and irregularly sampled measurements that present different uncertainty levels. An application to a real industrial case study shows that BCTTF presents better prediction performance, higher alarm identification sensitivity and leads to a smoother estimated signal.
Paper VI111-04.8 
PDF · Video · Dynamic Weighted Canonical Correlation Analysis for Auto-Regressive Modeling (I)

Zhu, QinqinUniversity of Waterloo
Liu, QiangNortheastern University
Qin, S. JoeUniversity of Southern California
Keywords: Fault detection and diagnosis, Machine learning, Time series modelling
Abstract: Canonical correlation analysis (CCA) is widely used as a supervised learning method to extract correlations between process and quality datasets. When used to extract relations between current data and historical data, CCA can also be regarded as an auto-regressive modeling method to capture dynamics. Various dynamic CCA algorithms were developed in the literature. However, these algorithms do not consider strong dependence existing in adjacent samples, which may lead to unnecessarily large time lags and inaccurate estimation of current values from historical data. In this paper, a dynamic weighted CCA (DWCCA) algorithm is proposed to address this issue with a series of polynomial basis functions. DWCCA extracts dynamic relations by maximizing correlations between current data and a weighted representation of past data, and the weights rely only on a limited number of polynomial functions, which removes the negative effect caused by strongly collinear neighboring samples. After all the dynamics are exploited, static principal component analysis is then employed to further explore the cross-correlations in the dataset. The Tennessee Eastman process is utilized to demonstrate the effectiveness of the proposed DWCCA method in terms of prediction efficiency and collinearity handling.
Paper VI111-04.9 
PDF · Video · Assessing Observability Using Supervised Autoencoders with Application to Tennessee Eastman Process (I)

Agarwal, PiyushUniversity of Waterloo
Tamer, MelihSanofi Pasteur
Budman, Hector M.Univ. of Waterloo
Keywords: Machine learning, Fault detection and diagnosis, Identifiability
Abstract: This work presents a novel approach to calculate classification observability using a supervised autoencoder (SAE) neural network (NN) for classification. This metric is based on a minimal distance between every two classes in the latent space defined by the hidden layers of the auto-encoder. Quantification of classification observability is required to address whether the available sensors in a process are sufficient to observe certain outputs (phenomenon) and which additional measurements are to be included in the dataset to improve classification accuracy. The efficacy of the proposed method is illustrated through case-studies for the Tennessee Eastman Benchmark Process.
Paper VI111-04.10 
PDF · Video · Study on a Sub-Databases-Driven (S-DD) Controller Using K-Means Clustering (I)

Wakitani, ShinHiroshima University
Nakanishi, HirokiHiroshima University
Yamamoto, ToruHiroshima Univ
Keywords: Machine learning, Learning for control, Adaptive gain scheduling autotuning control and switching control
Abstract: A database-driven PID (DD-PID) control method is one of the effective control methods for nonlinear systems. In the conventional DD-PID control method, there is a problem that the calculation cost and required memory for creating an optimal database are large. For the above problem, this paper proposes a method to implement the DD-PID controller with small-sized sub-databases. In the proposed method, one database that includes past I/O data and PID gains are created, and the database is updated in an offline manner. Moreover, sub-databases are constructed by clustering the created database using the k-means clustering method. The number of clusters for k-means clustering is determined automatically based on kernel functions. The effectiveness of the proposed method is presented by numerical examples.
Paper VI111-04.11 
PDF · Video · Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey (I)

Gopaluni, BhushanUniversity of British Columbia
Tulsyan, AdityaMassachusetts Institute of Technology
Chachuat, BenoitImperial College London
Huang, BiaoUniv. of Alberta
Lee, Jong MinSeoul National University
Amjad, FarazUniversity of Alberta
Damarla, SeshuUniversity of Alberta
Kim, Jong WooSeoul National University
Lawrence, Nathan P.University of British Columbia
Keywords: Machine learning, Consensus and Reinforcement learning control
Abstract: Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning. This opens up an opportunity to use modern machine learning tools on large-scale nonlinear monitoring and control problems. This article provides a survey of recent results with applications in the process industry.
Paper VI111-04.12 
PDF · Video · Reinforcement Learning Based Design of Linear Fixed Structure Controllers (I)

Lawrence, Nathan P.University of British Columbia
Stewart, Greg E.Honeywell Automation & Control Sol
Loewen, Philip D.Univ. of British Columbia
Forbes, Michael GregoryHoneywell
Backstrom, JohanHoneywell
Gopaluni, BhushanUniversity of British Columbia
Keywords: Learning for control, Randomized methods, Model reference adaptive control
Abstract: Reinforcement Learning has been successfully applied to the problem of tuning PID controllers in several applications. The existing methods often utilize function approximation, such as neural networks, to update the controller parameters at each time-step of the underlying process. In this work, we present a simple finite-difference approach, based on random search, to tuning linear fixed-structure controllers. For clarity and simplicity, we focus on PID controllers. Our algorithm operates on the entire closed-loop step-response of the system and iteratively improves the PID gains towards a desired closed-loop response. This allows for prescribing stability requirements into the reward function without any modeling procedures.
Paper VI111-04.13 
PDF · Video · Optimal PID and Antiwindup Control Design As a Reinforcement Learning Problem (I)

Lawrence, Nathan P.University of British Columbia
Stewart, Greg E.Honeywell Automation & Control Sol
Loewen, Philip D.Univ. of British Columbia
Forbes, Michael GregoryHoneywell
Backstrom, JohanHoneywell
Gopaluni, BhushanUniversity of British Columbia
Keywords: Learning for control, Consensus and Reinforcement learning control, Nonlinear adaptive control
Abstract: Deep reinforcement learning (DRL) has seen several successful applications to process control. Common methods rely on a deep neural network structure to model the controller or process. With increasingly complicated control structures, the closed-loop stability of such methods becomes less clear. In this work, we focus on the interpretability of DRL control methods. In particular, we view linear fixed-structure controllers as shallow neural networks embedded in the actor-critic framework. PID controllers guide our development due to their simplicity and acceptance in industrial practice. We then consider input saturation, leading to a simple nonlinear control structure. In order to effectively operate within the actuator limits we then incorporate a tuning parameter for anti-windup compensation. Finally, the simplicity of the controller allows for straightforward initialization. This makes our method inherently stabilizing, both during and after training, and amenable to known operational PID gains.
VI111-05
Modelling, Identification and Control of Quantum Systems Open Invited Session
Chair: Dong, DaoyiUniversity of New South Wales
Co-Chair: Wu, Re-bingDepartment of Automation, Tsinghua University,
Organizer: Dong, DaoyiUniversity of New South Wales
Organizer: Li, Jr-ShinWashington University in St. Louis
Organizer: Wu, Re-bingDepartment of Automation, Tsinghua University,
Paper VI111-05.1 
PDF · Video · Robust Control Optimization for Quantum Approximate Optimization Algorithms (I)

Dong, YulongUniversity of California, Berkeley
Meng, XiangUniversity of California, Berkeley
Lin, LinUniversity of California, Berkeley
Kosut, RobertSC Solutions
Whaley, K. BirgittaUC Berkeley
Keywords: Dynamic Networks
Abstract: Quantum variational algorithms have garnered significant interest recently, due to their feasibility of being implemented and tested on noisy intermediate scale quantum (NISQ) devices. We examine the robustness of the quantum approximate optimization algorithm (QAOA), which can be used to solve certain quantum control problems, state preparation problems, and combinatorial optimization problems. We demonstrate that the error of QAOA simulation can be significantly reduced by robust control optimization techniques, specifically, by sequential convex programming (SCP), to ensure error suppression in situations where the source of the error is known but not necessarily its magnitude. We show that robust optimization improves both the objective landscape of QAOA as well as overall circuit fidelity in the presence of coherent errors and errors in initial state preparation.
Paper VI111-05.2 
PDF · Video · Quantum Adiabatic Elimination at Arbitrary Order for Photon Number Measurement (I)

Sarlette, AlainINRIA
Rouchon, PierreMines-ParisTech, PSL Research University
Essig, AntoineENS Lyon
Ficheux, QuentinUniversity of Maryland
Huard, BenjaminENS Lyon
Keywords: Subspace methods
Abstract: Adiabatic elimination is a perturbative model reduction technique based on timescale separation and often used to simplify the description of composite quantum systems. We here analyze a quantum experiment where the perturbative expansion can be carried out to arbitrary order, such that: (i) we can formulate in the end an exact reduced model in quantum form; (ii) as the series provides accuracy for ever larger parameter values, we can discard any condition on the timescale separation, thereby analyzing the intermediate regime where the actual experiment is performing best; (iii) we can clarify the role of some gauge degrees of freedom in this model reduction technique.
Paper VI111-05.3 
PDF · Video · Optimal Quantum Realization of a Classical Linear System (I)

Thien, RebbeccaAustralian National University
Vuglar, ShanonPrinceton University
Petersen, Ian RThe Australian National University
Keywords: Continuous time system estimation, Identification for control, Dynamic Networks
Abstract: Additional noise in a quantum system can be detrimental to the performance of a quantum coherent feedback control system. This paper proposes a Linear Matrix Inequality (LMI) approach to construct an optimal quantum realization of a given Linear Time-Invariant (LTI) system. The quantum realization problem is useful in designing coherent quantum feedback controllers. An optimal method is proposed for solving this problem in terms of a finite horizon quadratic performance index, which is related to the amount of quantum noise appearing at the system's output. This cost function provides a measure of how much the additional quantum noise in the coherent controller will alter the feedback control system.
Paper VI111-05.4 
PDF · Video · Capability Comparison of Quantum Sensors of Single or Two Qubits for a Spin Chain System (I)

Yu, QiUNSW (The University of New South Wales)
Dong, DaoyiUniversity of New South Wales
Wang, YuanlongUniversity of New South Wales, Canberra
Petersen, Ian RThe Australian National University
Keywords: Identifiability
Abstract: Quantum sensing, utilizing quantum techniques to extract key information of a quantum (or classical) system, is a fundamental area in quantum science and technology. For quantum sensors, a basic capability is to uniquely infer unknown parameters in a system based on measurement data from the sensors. In this paper, we investigate the capability of a class of quantum sensors for a spin-1/2 chain system with unknown parameters. The sensors are composed of qubits which are coupled to the object system and can be initialized and measured. We consider the capability of the single- and two-qubit sensors and show that the capability of single-qubit quantum sensors can be enhanced by adding an extra qubit into the sensor under a certain initialization and measurement setting.
Paper VI111-05.5 
PDF · Video · Coherent H-Infinity Control for Markovian Jump Linear Quantum Systems (I)

Liu, YananUniversity of New South Wales
Dong, DaoyiUniversity of New South Wales
Petersen, Ian RThe Australian National University
Gao, QingBeihang University
Ding, Steven X.Univ of Duisburg-Essen
Yonezawa, HidehiroUniversity of New South Wales
Keywords: Fault detection and diagnosis
Abstract: The purpose of this paper is to design a coherent feedback controller for a Markovian jump linear quantum system suffering from a fault signal. The control objective is to bound the effect of the disturbance input on the output for the time-varying quantum system. We prove the relation between the H-infinity control problem, the dissipation properties, and the solutions of Riccati differential equations, by which the H-infinity controller of the Markovian jump linear quantum system is given by the solutions of Linear Matrix Inequalities (LMIs).
Paper VI111-05.6 
PDF · Video · Measurement-Induced Boolean Dynamics from Closed Quantum Networks (I)

Qi, HongshengChinese Academy of Sciences
Mu, BiqiangAMSS, CAS
Petersen, Ian RThe Australian National University
Shi, GuodongThe Australian National University/The University of Sydney
Keywords: Stochastic hybrid systems
Abstract: In this paper, we study the induced probabilistic Boolean dynamics for dynamical quantum networks subject to sequential quantum measurements. In this part of the paper, we focus on closed networks of quits whose states evolve according to a Schr"odinger equation. Sequential measurements may act on the entire network, or only on a subset of qubits. First of all, we show that this type of hybrid quantum dynamics induces probabilistic Boolean recursions as a Markov chain representing the measurement outcomes. Particularly, we establish an explicit and algebraic representation of the underlying recursive random mapping driving such induced Markov chains. Next, with local measurements, we establish a recursive way of computing such non-Markovian probability transitions.
Paper VI111-05.7 
PDF · Video · Measurement-Induced Boolean Dynamics from Open Quantum Networks (I)

Qi, HongshengChinese Academy of Sciences
Mu, BiqiangAMSS, CAS
Petersen, Ian RThe Australian National University
Shi, GuodongThe Australian National University/The University of Sydney
Keywords: Stochastic hybrid systems
Abstract: In this paper, we study the induced probabilistic Boolean dynamics for dynamical quantum networks subject to sequential quantum measurements. In this part of the paper, we focus on closed networks of quits whose states evolutions are described by a continuous Lindblad master equation. When measurements are performed sequentially along such continuous dynamics, the quantum network states undergo random jumps and the corresponding measurement outcomes can be described by a probabilistic Boolean network. First of all, we show that the state transition of the induced Boolean networks can be explicitly represented through realification of the master equation. Next, when the open quantum dynamics is relaxing in the sense that it possesses a unique equilibrium as a global attractor, structural properties including absorbing states, reducibility, and periodicity for the induced Boolean network are direct consequences of the relaxing property. Finally, we show that for quantum consensus networks as a type of non-relaxing open quantum network dynamics, the communication classes of the measurement-induced Boolean networks are encoded in the quantum Laplacian of the underlying interaction graph.
Paper VI111-05.8 
PDF · Video · Positive Real Properties and Physical Realizability Conditions for a Class of Linear Quantum Systems (I)

Maalouf, AlineThe Australian National University
Petersen, Ian RThe Australian National University
Keywords: Estimation and filtering
Abstract: Theoretical developments in the field of quantum optics and quantum superconducting electrical circuits involving continuous measurement based feedback control as well as coherent control are an important prerequisites for advances in the domain of quantum technology. Within these perspectives, this paper considers positive real properties for a class of quantum systems whose quantum stochastic differential equation model involves annihilation operators only and then relates them to corresponding bounded real properties and consequently to physical realizability conditions developed earlier by the authors. Based on the positive real properties of these quantum systems, it is anticipated that it is possible to use the Brune algorithm in order to find an electrical circuit that can physically implement these quantum systems. This theory, in the case of one-port circuits, may be useful for the implementation of microwave circuits related to quantum filters found in the field of quantum computing.
Paper VI111-05.9 
PDF · Video · One Port Impedance Quantization for a Class of Annihilation Operator Linear Quantum Systems (I)

Maalouf, AlineThe Australian National University
Petersen, Ian RThe Australian National University
Keywords: Estimation and filtering;Quantized systems
Abstract: This paper provides a procedure for building a one port impedance quantization involving annihilation operators only for a class of linear quantum systems having a positive real impedance transfer function matrix. Based on the positive real properties of these quantum systems, it is shown that it is possible to use the Brune algorithm in order to find an electrical circuit that can physically implement these quantum systems. This theory, illustrated for one port circuits may be useful for the implementation of superconducting microwave circuits used in quantum filters found in the field of quantum computing.
Paper VI111-05.10 
PDF · Video · Frequency-Domain Computation of Quadratic-Exponential Cost Functionals for Linear Quantum Stochastic Systems (I)

Vladimirov, IgorAustralian National University
Petersen, Ian RThe Australian National University
James, Matthew R.Australian National Univ
Keywords: Stochastic control and game theory, Synthesis of stochastic systems
Abstract: This paper is concerned with quadratic-exponential functionals (QEFs) as risk-sensitive performance criteria for linear quantum stochastic systems driven by multichannel bosonic fields. Such costs impose an exponential penalty on quadratic functions of the quantum system variables over a bounded time interval, and their minimization secures a number of robustness properties for the system. We use an integral operator representation of the QEF, obtained recently, in order to compute its infinite-horizon asymptotic growth rate in the invariant Gaussian state when the stable system is driven by vacuum input fields. The resulting frequency-domain formula expresses the QEF growth rate in terms of two spectral functions associated with the real and imaginary parts of the quantum covariance kernel of the system variables. We also discuss the computation of the QEF growth rate using homotopy and contour integration techniques and provide an illustrative numerical example with a two-mode open quantum harmonic oscillator.
Paper VI111-05.11 
PDF · Video · The Dynamical Model of Flying-Qubit Control Systems (I)

Li, WenlongTsinghua University
Zhang, GuofengThe Hong Kong Polytechnic University
Wu, Re-bingDepartment of Automation, Tsinghua University,
Keywords: Stochastic control and game theory
Abstract: The control of flying qubits is crucial for the nterconnection of quantum information processing units in the future applications. Physically, this class of problems can be modeled by the radiation of optical elds from a standing qubit (natural or artificial atoms). The photon statistics of the output field emitted from a quantum system coupled to multiple waveguides is complicated when the exciton number is not conserved, especially in presence of coherent driving that is crucial for control and optimization. In this paper, we use quantum stochastic differential equation (QSDE) todescribe the photon generation process, and derive the dynamical jumps induced by photon emission. Numerical simulations show that this model can be applied to analyze the manipulation process of single qubits.
Paper VI111-05.12 
PDF · Video · Measurement-Based Feedback Control of Linear Quantum Stochastic Systems with Quadratic-Exponential Criteria (I)

Vladimirov, IgorAustralian National University
James, Matthew R.Australian National Univ
Petersen, Ian RThe Australian National University
Keywords: Synthesis of stochastic systems, Stochastic control and game theory
Abstract: This paper is concerned with a risk-sensitive optimal control problem for a feedback connection of a quantum plant with a measurement-based classical controller. The plant is a multimode open quantum harmonic oscillator driven by a multichannel quantum Wiener process, and the controller is a linear time invariant system governed by a stochastic differential equation. The control objective is to stabilize the closed-loop system and minimize the infinite-horizon asymptotic growth rate of a quadratic-exponential functional (QEF) which penalizes the plant variables and the controller output. We combine a frequency-domain representation of the QEF growth rate, obtained recently, with variational techniques and establish first-order necessary conditions of optimality for the state-space matrices of the controller.
VI111-06
Results on Nonlinear System Identification Benchmarks Open Invited Session
Chair: Schoukens, MaartenEindhoven University of Technology
Co-Chair: Noël, Jean-PhilippeEindhoven University of Technology
Organizer: Schoukens, MaartenEindhoven University of Technology
Organizer: Noël, Jean-PhilippeEindhoven University of Technology
Paper VI111-06.1 
PDF · Video · On the Initialization of Nonlinear LFR Model Identification with the Best Linear Approximation (I)

Schoukens, MaartenEindhoven University of Technology
Tóth, RolandEindhoven University of Technology
Keywords: Nonlinear system identification
Abstract: Balancing the model complexity and the representation capability towards the process to be captured remains one of the main challenges in nonlinear system identification. One possibility to reduce model complexity is to impose structure on the model representation. To this end, this work considers the linear fractional representation framework. In a linear fractional representation the linear dynamics and the system nonlinearities are modeled by two separate blocks that are interconnected with one another. This results in a structured, yet flexible model structure. Estimating such a model directly from input-output data is not a trivial task as the involved optimization is nonlinear in nature. This paper proposes an initialization scheme for the model parameters based on the best linear approximation of the system and shows that this approach results in high quality models on a set of benchmark data sets.
Paper VI111-06.2 
PDF · Video · A Novel Multiplicative Polynomial Kernel for Volterra Series Identification (I)

Dalla Libera, AlbertoUniversità Degli Studi Di Padova
Carli, RuggeroUniv of Padova
Pillonetto, GianluigiUniv of Padova
Keywords: Nonparametric methods, Nonlinear system identification, Time series modelling
Abstract: Volterra series is especially useful for nonlinear system identification, also thanks to is capability to approximate a broad range of input-output maps. However, its identification from a finite set of data is hard, due to the curse of dimensionality. Recent approaches have shown how regularization strategies can be useful for this task. In this paper, we propose a new regularization network for Volterra models identification. It relies on a new kernel given by the product of basic building blocks. Each block contains some unknown parameters that can be estimated from data using marginal likelihood optimization or cross-validation. In comparison with other algorithms proposed in the literature, numerical experiments show that our approach allows to better select the monomials that really influence the system output, much increasing the prediction capability of the model. The method immediately extends also to polynomial NARMAX models.
Paper VI111-06.3 
PDF · Video · Data-Driven Modelling of the Nonlinear Cortical Responses Evoked by Continuous Mechanical Perturbations (I)

Nozari, Hasan AbbasiFaculty of Electrical and Computer Engineering, Babol Noshirvani
Rahmani, ZahraBabol Noshirvani University of Technology
Castaldi, PaoloUniversity of Bologna
Simani, SilvioUniversity of Ferrara
Sadati, JalilFaculty of Electrical and Computer Engineering, Babol Noshirvani
Keywords: Nonlinear system identification, Identification for control, Frequency domain identification
Abstract: Cortical responses to external mechanical stimuli recorded by electroencephalography have demonstrated complex nonlinearity with fast dynamics. Hence, the modelling of the human nervous system plays a crucial role in studying the function of the sensorimotor system and can help in disentangling the sensory-motor abnormalities in functional movement disorders. In this paper, a non-parametric model is proposed based on locally-linear neuro-fuzzy structures trained by an evolutive algorithm named local linear model tree. In particular, a simulation model as well as a multi-step predictor model is considered to describe the nonlinear dynamics governing the cortical response. The proposed modelling method is applied to an experimental dataset, where brain activities from ten young healthy subjects are recorded by electroencephalography signals while robotic manipulations were applied to their wrist joint. The obtained results are satisfactory and are also compared to those achieved with different modelling strategies applied to the same benchmark.
Paper VI111-06.4 
PDF · Video · Initialization Approach for Decoupling Polynomial NARX Model Using Tensor Decomposition (I)

Karami, KianaUniversity of Calgary
Westwick, DavidUniversity of Calgary
Keywords: Nonlinear system identification
Abstract: The Nonlinear Auto-regressive eXogenous input (NARX) model has been widely used in nonlinear system identification. It's chief disadvantages are that it is a black-box model that suffers from the curse of dimensionality, in that the number of parameters increases rapidly with the nonlinearity degree. One approach to dealing with these problems involves decoupling the nonlinearity, but this requires solving a non-convex optimization problem. Solving non-convex optimization problems has always been challenging due to the possibility of getting trapped in a sub-optimal local optima. As a result, these kinds of optimization problems are sensitive to the initial solution. Providing an appropriate initial solution can increase the likelihood of finding the globally optimal solution. In this paper, an initialization technique that uses the polynomial coefficients in a full, albeit low order, NARX model is proposed. This technique generates a tensor from the coefficients in the from full polynomial NARX model and applies a tensor factorization in order to generate an appropriate starting point for decoupled polynomial NARX model optimization problem. The proposed technique is applied to nonlinear benchmark problem and the results are promising.
Paper VI111-06.5 
PDF · Video · Tuning Nonlinear State-Space Models Using Unconstrained Multiple Shooting (I)

Decuyper, JanVrije Universiteit Brussel
Runacres, Mark CVrije Universiteit Brussel
Schoukens, JohanVrije Universiteit Brussel
Tiels, KoenEindhoven University of Technology
Keywords: Nonlinear system identification
Abstract: A persisting challenge in nonlinear dynamical modelling is parameter inference from data. Provided that an appropriate model structure was selected, the identification problem is profoundly affected by a choice of initialisation. A particular challenge that may arise is initialisation within a region of the parameter space where the model is not contractive. Exploring such regions is not feasible using the conventional optimisation tools for they require a bounded evaluation of the cost. This work proposes an unconstrained multiple shooting technique, able to mitigate stability issues during the optimisation of nonlinear state-space models. The technique is illustrated on simulation results of a Van der Pol oscillator and benchmark results on a Bouc-Wen hysteretic system.
VI111-07
Application of System Identification Regular Session
Chair: Jampana, PhanindraIndian Institute of Technology Hyderabad
Co-Chair: Petlenkov, EduardTallinn University of Technology
Paper VI111-07.1 
PDF · Video · Assessment Criteria for the Mechanical Loads of Wind Turbines Applied to the Example of Active Power Control (I)

Clemens, ChristianUniversity of Applied Sciences Berlin (HTW), Department of Engin
Gauterin, EckhardHTW Berlin
Pöschke, FlorianUniversity of Applied Sciences Berlin (HTW), Control Engineering
Schulte, HorstHTW Berlin
Keywords: Experiment design
Abstract: Assessment criteria for design of wind turbines controller are discussed since conventional control performance criteria are not sufficient to evaluate the mechanical loads as dependency of the controller type and settings. This will be presented and discussed using the example of the active power control of wind turbines. In contrast to the nominal operation of wind turbines divided into power optimization in the partial and power limitation in the full load region, the power output is guided by an external power reference signal. The reference signal may be delivered either directly by higher-level load frequency controller of the power system or by the wind farm controller. In both cases the external variation of the power to be delivered has an enormous influence on the dynamics and mechanical loads of the wind turbine. To quantify these loads that occur during power tracking operation the Damage Equivalent Load amplitude as appropriated load assessment criteria is described and prepared for control design.
Paper VI111-07.2 
PDF · Video · Simulation of RF Noise Propagation to Relativistic Electron Beam Properties in a Linear Accelerator

Maalberg, AndreiHelmholtz-Zentrum Dresden-Rossendorf
Kuntzsch, MichaelHelmholtz-Zentrum Dresden-Rossendorf
Petlenkov, EduardTallinn University of Technology
Keywords: Frequency domain identification
Abstract: The control system of the superconducting electron linear accelerator ELBE is planned to be upgraded by a beam-based feedback. As the design of the feedback algorithm enters its preliminary stage, the problem of analyzing the contribution of various disturbances to the development of the electron beam instabilities becomes highly relevant. In this paper we exploit the radio frequency (RF) phase and amplitude noise data measured at ELBE to create a behavioral model in Simulink. By modeling the interaction between a RF electromagnetic field and an electron bunch traversing a bunch compressor we analyze how the addition of RF noise impacts the electron beam properties, such as energy, duration and arrival time.
Paper VI111-07.3 
PDF · Video · Sparsity Constrained Reconstruction for Electrical Impedance Tomography

Theertham, Ganesh TejaIndian Institute of Technology Hyderabad
Varanasi, Santhosh KumarUniversity of Alberta
Jampana, PhanindraIndian Institute of Technology Hyderabad
Keywords: Mechanical and aerospace estimation, Errors in variables identification
Abstract: Electrical Impedance Tomography (EIT) can be used to study the hydrodynamic characteristics in multiphase flows such as gas holdup in bubble columns, air core in hydrocyclone etc. In EIT, the main objective is to estimate the electrical properties (conductivity distribution) of an object in a region of interest based on the surface voltage measurements. The main challenge in such reconstruction (estimation of conductivity distribution) is the low spatial resolution. In this paper, a sparse optimization approach for image reconstruction in EIT is presented. The main idea presented in this article is based on considering the L1 norm on the data term which enhances reconstruction of conductivity distributions with sharp changes near phase boundaries. Further, this method is also robust to outliers in the data. The accuracy of the proposed method is demonstrated with the help of two phantoms and a comparison with the existing methods is also presented.
Paper VI111-07.4 
PDF · Video · Integral Resonance Control in Continuous Wave Superconducting Particle Accelerators

Bellandi, AndreaDeutsches Elektronen-Synchrotron (DESY)
Branlard, JulienDESY
Eichler, AnnikaDESY
Pfeiffer, SvenDESY Hamburg
Keywords: Time series modelling
Abstract: Superconducting accelerating cavities for continuous wave low-current particle accelerators requires a tight resonance control to optimize the RF power costs and to minimize the beam delivery downtime. When the detuning produced by radiation pressure becomes comparable to the RF bandwidth, the monotonic instability starts to affect the cavity operation. When this instability is triggered by external vibrations or drifts, the accelerating field amplitude drops rapidly, and the beam acceleration has to be stopped. Past experiments showed that using an integral control of the piezoelectric tuners installed on the cavity prevents the adverse effects of the monotonic instability. This paper derives theoretically why an integral controller is an effective way to counteract the monotonic instability. To perform the study a linearized state-space model of the cavity is derived. Simulations and experiments in a superconducting test facility indicate that the use of this kind of control has the additional benefit of bringing the cavities to the resonance condition automatically.
VI111-08
Bayesian Methods Regular Session
Chair: Hjalmarsson, HåkanKTH
Co-Chair: Iannelli, AndreaETH Zurich
Paper VI111-08.1 
PDF · Video · A Novel Robust Kalman Filter with Non-Stationary Heavy-Tailed Measurement Noise

Jia, GuangleHarbin Engineering University
Huang, YulongHarbin Engineering University
Bai, MingmingHarbin Engineering University
Zhang, YonggangHarbin Engineering University
Keywords: Bayesian methods, Filtering and smoothing
Abstract: A novel robust Kalman filter based on Gaussian-Student's t mixture (GSTM) distribution is proposed to address the filtering problem of a linear system with non-stationary heavy-tailed measurement noise. The mixing probability is recursively estimated by using its previous estimates as prior information, and the state vector, the auxiliary parameter, the Bernoulli random variable and the mixing probability are jointly estimated utilizing the variational Bayesian method. The excellent performance of the proposed robust Kalman filter, compared with the existing state-of-the-art filters, is illustrated by a target tracking simulation results under the case of non-stationary heavy-tailed measurement noise.
Paper VI111-08.2 
PDF · Video · Stochastic Input Design Problems for the Frequency Response in Bayesian Identification

Zheng, ManKyoto University
Ohta, YoshitoKyoto University
Keywords: Bayesian methods, Frequency domain identification, Input and excitation design
Abstract: Recently, the research of identification input design for Bayesian methods has been actively investigated. Either the problem is formulated as a non-convex problem with difficulty in solving or relaxed as a convex problem with a price of some conservativeness. In this contribution, a new minimum power input design problem is formulated by viewing the input as a stochastic process. We seek the minimum energy input with variance constraints over a frequency band. By exploiting the generalized Kalman-Yakubovich-Popov lemma, the stochastic consideration facilitates the input design problem to be presented as a convex problem whose decision variables are a finite number of autocorrelation coefficients. We obtain the autocorrelation coefficients of the desired stochastic input signal by solving the convex problem and extend them by the maximum entropy extension. Then, a specific identification input is sampled from the obtained stochastic process. Simulations results demonstrate the effectiveness of the proposed method.
Paper VI111-08.3 
PDF · Video · Cascade Control: Data-Driven Tuning Approach Based on Bayesian Optimization

Khosravi, MohammadETH Zurich
Behrunani, VarshaETH Zurich. Automatic Control Laboratory
Smith, Roy S.Swiss Federal Institute of Technology (ETH)
Rupenyan, AlisaETH Zurich
Lygeros, JohnETH Zurich
Keywords: Bayesian methods, Learning for control, Fault detection and diagnosis
Abstract: Cascaded controller tuning is a multi-step iterative procedure that needs to be performed routinely upon maintenance and modification of mechanical systems. An automated data-driven method for cascaded controller tuning based on Bayesian optimization is proposed. The method is tested on a linear axis drive, modeled using a combination of first principles model and system identification. A custom cost function based on performance indicators derived from system data at different candidate configurations of controller parameters is modeled by a Gaussian process. It is further optimized by minimization of an acquisition function which serves as a sampling criterion to determine the subsequent candidate configuration for experimental trial and improvement of the cost model iteratively, until a minimum according to a termination criterion is found. This results in a data-efficient procedure that can be easily adapted to varying loads or mechanical modifications of the system. The method is further compared to several classical methods for auto-tuning, and demonstrates higher performance according to the defined data-driven performance indicators. The influence of the training data on a cost prior on the number of iterations required to reach optimum is studied, demonstrating the efficiency of the Bayesian optimization tuning method.
Paper VI111-08.4 
PDF · Video · Parameter Identification for Digital Fabrication: A Gaussian Process Learning Approach

Stürz, Yvonne RebeccaUniversity of California Berkeley
Khosravi, MohammadETH Zurich
Smith, Roy S.Swiss Federal Institute of Technology (ETH)
Keywords: Bayesian methods, Learning for control, Nonlinear system identification
Abstract: Tensioned cable nets can be used as supporting structures for the efficient construction of lightweight building elements, such as thin concrete shell structures. To guarantee important mechanical properties of the latter, the tolerances on deviations of the tensioned cable net geometry from the desired target form are very tight. Therefore, the form needs to be readjusted on the construction site. In order to employ model-based optimization techniques, the precise identification of important uncertain model parameters of the cable net system is required. This paper proposes the use of Gaussian process regression to learn the function that maps the cable net geometry to the uncertain parameters. In contrast to previously proposed methods, this approach requires only a single form measurement for the identification of the cable net model parameters. This is beneficial since measurements of the cable net form on the construction site are very expensive. For the training of the Gaussian processes, simulated data is efficiently computed via convex programming. The effectiveness of the proposed method and the impact of the precise identification of the parameters on the form of the cable net are demonstrated in numerical experiments on a quarter-scale prototype of a roof structure.
Paper VI111-08.5 
PDF · Video · Robust Gaussian Process Regression with G-Confluent Likelihood

Lindfors, MartinLinköping University
Chen, TianshiThe Chinese University of Hong Kong, Shenzhen, China
Keywords: Bayesian methods, Machine learning
Abstract: For robust Gaussian process regression problems where the measurements are contaminated by outliers, a likelihood/measurement noise model with heavy-tailed distributions should be used to improve the prediction performance. In this paper, we propose to use a G-confluent distribution as the measurement noise model and a coordinate ascent variational inference method to infer the overall statistical model. In contrast with the commonly used Student's t distribution, the G-confluent distribution can also be written as a Gaussian scale mixture, but its inverse scale follows a Beta distribution rather than a Gamma distribution, and its main advantage is that it is more flexible for modeling outliers while being equally suitable for variational inference. Numerical simulations based on benchmark data show that the G-confluent distribution performs better than or as well as the Student's t distribution.
Paper VI111-08.6 
PDF · Video · Nonparametric Models for Hammerstein-Wiener and Wiener-Hammerstein System Identification

Risuleo, Riccardo SvenKTH Royal Institute of Technology
Hjalmarsson, HåkanKTH
Keywords: Bayesian methods, Nonlinear system identification, Nonparametric methods
Abstract: We propose a framework for modeling structured nonlinear systems using nonparametric Gaussian processes. In particular, we introduce a two-layer stochastic model of latent interconnected Gaussian processes suitable for modeling Hammerstein-Wiener and Wiener-Hammerstein cascades. The posterior distribution of the latent processes is intractable because of the nonlinear interactions in the model; hence, we propose a Markov Chain Monte Carlo method consisting of a Gibbs sampler where each step is implemented using elliptical-slice sampling. We present the results on two example nonlinear systems showing that they can effectively be modeled and identified using the proposed nonparametric modeling approach.
Paper VI111-08.7 
PDF · Video · Regularized System Identification: A Hierarchical Bayesian Approach

Khosravi, MohammadETH Zurich
Iannelli, AndreaETH Zurich
Yin, MingzhouETH Zurich
Parsi, AnilkumarETH Zurich
Smith, Roy S.Swiss Federal Institute of Technology (ETH)
Keywords: Bayesian methods, Nonparametric methods, Machine learning
Abstract: In this paper, the hierarchical Bayesian method for regularized system identification is introduced. To this end, a hyperprior distribution is considered for the regularization matrix and then, the impulse response and the regularization matrix are jointly estimated based on a maximum a posteriori (MAP) approach. Toward introducing a suitable hyperprior, we decompose the regularization matrix using Cholesky decomposition and reduce the estimation problem to the cone of upper triangular matrices with positive diagonal entries. Following this, the hyperprior is introduced on a designed sub-cone of this set. The method differs from the current trend in regularized system identification from various aspect, e.g., the estimation is performed by solving a single stage problem. The MAP estimation problem reduces to a multi-convex optimization problem and a sequential convex programming algorithm is introduced for solving this problem. Consequently, the proposed method is a computationally efficient strategy specially when the regularization matrix has a large size. The method is numerically verified on benchmark examples. Owing to the employed full Bayesian approach, the estimation method shows a satisfactory bias-variance trade-off.
Paper VI111-08.8 
PDF · Video · Low-Complexity Identification by Sparse Hyperparameter Estimation

Khosravi, MohammadETH Zurich
Yin, MingzhouETH Zurich
Iannelli, AndreaETH Zurich
Parsi, AnilkumarETH Zurich
Smith, Roy S.Swiss Federal Institute of Technology (ETH)
Keywords: Bayesian methods, Nonparametric methods, Machine learning
Abstract: This paper presents a novel kernel-based system identification method, which promotes low complexity of the model in terms of the McMillan degree of the system. The regularization matrix is characterized as a linear combination of pre-selected rank-one matrices with unknown hyperparameter coefficients, and the hyperparameters are derived using a maximum a posteriori estimation approach. Each basis matrix is the optimal regularization matrix for a first-order system. With this basis matrix selection, the McMillan degree of the identified model is upper-bounded by the rank of the regularization matrix, which in turn is equal to the cardinality of the hyperparameters. For this reason, a sparsity-promoting prior is chosen for hyperparameter tuning. The resulting optimization problem has a difference of convex program form which can be efficiently solved. The advantages of the proposed method are that the identified model has a low-complexity structure and that an improved bias-variance trade-off is achieved. Numerical results confirm that the proposed method achieves a better bias-variance trade-off as well as a better fit to the model compared to both the empirical Bayes method and the atomic-norm regularization.
Paper VI111-08.9 
PDF · Video · A Two-Stage Algorithm for Estimation of Unknown Parameters Using Nonlinear Measurements

Stepanov, O.A.Concern CSRI Elektropribor, JSC; University ITMO
Nosov, AlekseiConcern CSRI Elektropribor, JSC; University ITMO
Keywords: Bayesian methods, Particle filtering/Monte Carlo methods, Filtering and smoothing
Abstract: A suboptimal two-stage algorithm has been proposed to solve nonlinear estimation problems consist in comparison of measured and reference samples. The new algorithm consists of preliminary processing of measurements, subsampling and simplification of the errors model in nonlinear algorithm. A significant increase in computational performance determines the novelty of the presented algorithm. The effective application of the two-stage suboptimal algorithm is illustrated by an example of gravity-aided navigation.
Paper VI111-08.10 
PDF · Video · Process Monitoring with Sparse Bayesian Model for Industrial Methanol Distillation

Luo, LinLiaoning Shihua University
Xie, LeiZhejiang University
Su, HongyeZhejiang University
Zeng, JiusunChina Jiliang University
Keywords: Fault detection and diagnosis, Bayesian methods
Abstract: Following the intuition that not all latent variables in probabilistic principal component analysis method shifts simultaneously, this paper proposes a spike-and-slab regularization technique for nonlinear fault detection and isolation. Different from the existing probabilistic latent variable models, a spike-and-slab prior is introduced to downweight the irrelevant information of latent variables for the discriminative model. The resulting latent subspace supported by regularization parameters is not only sensitive to the informative variables, but it also eliminates the influence of the non-informative ones. The feasibility and efficiency of the proposed approach will be tested on an industrial methanol distillation dataset. Moreover, the performance will be compared with conventional probabilistic latent variables methods.
Paper VI111-08.11 
PDF · Video · Probabilistic H2-Norm Estimation Via Gaussian Process System Identification

Persson, DanielUniversity of Stuttgart
Koch, AnneUniversity of Stuttgart
Allgower, FrankUniversity of Stuttgart
Keywords: Nonparametric methods, Bayesian methods, Identification for control
Abstract: We present a method for data-based estimation of the H2-norm of a linear time-invariant system from input-output data in a probabilistic setting by employing the recent advances in Gaussian process system identification using stable-spline kernels. Advantages of this starting point include that the norm can be estimated for the continuous-time system and over infinite horizon, even though only a finite number of measurements are available. We approximate the H2-norm distribution as Gaussian, whose expectation can even be obtained analytically, while we use a numerical scheme based on Gaussian process quadrature for the variance. Not only do we utilize the posterior variance of the Gaussian process to derive an error estimate for the H2-norm, but also to tune the estimation by optimizing the input sequence. The performance of the developed scheme is thoroughly evaluated in simulation.
Paper VI111-08.12 
PDF · Video · System Identification and Control of a Polymer Reactor

Münker, TobiasUniversity of Siegen
Kampmann, GerittUniversity of Siegen
Schüssler, MaxUniversity of Siegen
Nelles, OliverUniversity of Siegen
Keywords: Identification for control, Nonlinear system identification, Bayesian methods
Abstract: In a polymer production process, a special reactor is used to adjust the viscosity, i.e., chain length of the polymer. This reactor has several control variables mainly in manually control. For future automatic control concepts, such a reactor is modeled from data with a linear (regularized FIR) and a nonlinear state space model (LMSSN). A model predictive control approach is presented in simulation.
Paper VI111-08.13 
PDF · Video · Controllability Gramian of Nonlinear Gaussian Process State Space Models with Application to Model Sparsification

Kashima, KenjiKyoto University
Imai, MisakiKyoto University
Keywords: Learning for control, Bayesian methods, Stochastic system identification
Abstract: For linear control systems, the so-called controllability Gramian has played an important role to quantify how effectively the dynamical states can be driven to a target one by a suitable driving input. On the other hand, thanks to the availability of Big Data, the Gaussian process state space model, a data-driven probabilistic modeling framework, has attracted much attention in recent years. In this paper, we newly introduce the concept of the controllability Gramian for nonlinear dynamics represented by the Gaussian process state space model, aiming at better understanding of this new modeling framework. Then, its effective calculation method and application to model sparsification are investigated.
Paper VI111-08.14 
PDF · Video · On Gaussian Process Based Koopman Operators

Lian, YingzhaoEPFL
Jones, Colin N.Ecole Polytechnique Federale De Lausanne (EPFL)
Keywords: Learning for control, Nonlinear system identification, Bayesian methods
Abstract: Enabling analysis of non-linear systems in linear form, the Koopman operator has been shown to be a powerful tool for system identification and controller design. However, current data-driven methods cannot provide quantification of model uncertainty given the learnt model. This work proposes a probabilistic Koopman operator model based on Gaussian processes which extends the author’s previous results and gives a quantification of model uncertainty. The proposed probabilistic model enables efficient propagation of uncertainty in feature space which allows efficient stochastic/robust controller design. The proposed probabilistic model is tested by learning stable nonlinear dynamics generating hand-written characters and by robust controller design of a bilinear DC motor.
Paper VI111-08.15 
PDF · Video · A Kriging-Based Interacting Particle Kalman Filter for the Simultaneous Estimation of Temperature and Emissivity in Infra-Red Imaging

Toullier, ThibaudInria
Dumoulin, JeanIFSTTAR
Mevel, LaurentINRIA
Keywords: LPV system identification, Particle filtering/Monte Carlo methods, Bayesian methods
Abstract: Temperature estimation through infrared thermography is facing the lack of knowledge of the observed material's emissivity. The derivation of the physical equations lead to an ill-posed problem. A new Kriged Interacting Particle Kalman Filter is proposed. A state space model relates the measurements to the temperature and the Kalman filter equations yield a filter tracking the temperature over time. Moreover, a particle filter associated to Kriging prediction is interacting with a bank of Kalman filters to estimate the time-varying parameters of the system. The efficiency of the algorithm is tested on a simulated sequence of infrared thermal images.
Paper VI111-08.16 
PDF · Video · On Semiseparable Kernels and Efficient Computation of Regularized System Identification and Function Estimation

Chen, TianshiThe Chinese University of Hong Kong, Shenzhen, China
Andersen, Martin S.Technical University of Denmark
Keywords: Nonparametric methods, Bayesian methods
Abstract: A long-standing problem for kernel-based regularization methods is their high computational complexity O(N^3), where N is the number of data points. In this paper, we show that for semiseparable kernels and some typical input signals, their computational complexity can be lowered to O(Nq2), where q is the output kernel’s semiseparability rank that only depends on the chosen kernel and the input signal.
VI111-09
Classification, Estimation, and Filtering Regular Session
Chair: Hanebeck, UweKarlsruhe Institute of Technology (KIT)
Co-Chair: Nair, Girish N.University of Melbourne
Paper VI111-09.1 
PDF · Video · Multi-Point Search Based Identification under Severe Numerical Conditions

Sun, LianmingThe University of Kitakyushu
Uto, RyokaThe University of Kitakyushu
Liu, XinyuThe University of Kitakyushu
Sano, AkiraKeio University
Keywords: Closed loop identification, Estimation and filtering
Abstract: It is necessary to perform the system identification under severe numerical conditions in many practical applications. When less external test signals are available for parameter estimation from experimental data, the identification performance often suffers from numerical problems in the optimization procedure due to the less independent informative components, the influence of complicated noise, or the local minima problem. In this paper, a multi-point search based identification algorithm is investigated for system identification under severe numerical conditions. It introduces the output over-sampling scheme to collect the experimental input-output data, and extracts the information in time and space domains to complement information criterion for numerical optimization. Furthermore, the multi-point search is utilized to decrease the influence of local minima. The numerical simulation examples illustrate that the identification performance has been improved in the proposed algorithm.
Paper VI111-09.2 
PDF · Video · Position and Speed Estimation of PMSMs Using Gaussian Processes

Mayer, JanaKarlsruhe Institute of Technology
Basarur, AjitKarlsruhe Institute of Technology
Petrova, MarianaKarlsruhe Institute of Technology
Sordon, FabianKarlsruhe Institute of Technology
Zea, AntonioKarlsruhe Institute of Technology
Hanebeck, UweKarlsruhe Institute of Technology (KIT)
Keywords: Experiment design, Machine learning, Estimation and filtering
Abstract: In this paper, we present a novel low-cost technique to estimate both the position and the speed of a permanent magnet synchronous motor (PMSM) by sensing its stray magnetic field. At an optimal radial and axial distance, a low-cost magnetoresistive sensor is placed outside at the back of the PMSM. The magnetic field values are recorded for one complete rotor revolution at a resolution of less than a degree for different speeds of operation. Gaussian Processes (GPs) are employed to find a mapping function between the magnetic field values of the permanent magnet and the absolute angular positions. Then, by using the learned GP as a measurement function with an Extended Kalman Filter (EKF), both the angular position and speed of a PMSM can be estimated efficiently. Furthermore, we observe that the magnetic field depends not only on the position but also on the angular speed. To address this, we extend the GP to incorporate multivariate inputs. In order to take the periodicity of the data into account, we employ a periodic kernel for the GP. Additionally, a linear basis function model (LBFM) is introduced to incorporate more training points while maintaining the same computational cost. The GP and LBFM approaches are evaluated with data from a real PMSM experiment setup, and the accuracy of the position and speed state estimation is verified against a high-resolution optical encoder used as ground truth.
Paper VI111-09.3 
PDF · Video · On the Stable Cholesky Factorization-Based Method for the Maximum Correntropy Criterion Kalman Filtering

Kulikova, Maria V.Instituto Superior Técnico, Universidade De Lisboa
Keywords: Filtering and smoothing, Estimation and filtering, Bayesian methods
Abstract: This paper continues the research devoted to the design of numerically stable square-root implementations for the maximum correntropy criterion Kalman filtering (MCC-KF). In contrast to the previously obtained results, here we reveal the first robust (with respect to round-off errors) method within the Cholesky factorization-based approach. The method is formulated in terms of square-root factors of the covariance matrices, i.e. it belongs to the covariance-type filtering methodology. Additionally, a numerically stable orthogonal transformation is utilized at each iterate of the algorithm for accurate propagation of the Cholesky factors involved. The results of numerical experiments illustrate a superior performance of the novel MCC-KF implementation compared to both the conventional algorithm and its previously published Cholesky-based variant.
Paper VI111-09.4 
PDF · Video · An Adaptive Radiometric Meter with Variable Measurement Time for Monitoring of Coal Jigs Operation

Joostberens, JaroslawSilesian University of Technology
Cierpisz, StanislawInstitute of Innovative Technologies EMAG
Keywords: Filtering and smoothing, Estimation and filtering, Errors in variables identification
Abstract: The authors discuss the problem of how to monitor the coal/water pulsating bed in a jig with the use of a radiation density meter. The dynamic measurement error of changes in density depends on the time of measurement; its optimal value can be found for a given shape of density changes. An alternative method of the signal filtration is proposed using variable time of measurement during a cycle of pulsations as a function of the time derivative of the density changes. The shape of the density changes during one cycle varies slowly from a cycle to a cycle. This is why the time derivative of the density determined during one cycle can be used in the subsequent cycle to adapt periodically the algorithm generating the variable times of measurement during each cycle. The above time derivative can be calculated from the polynomial fit of stochastic data measured during the previous cycle. In this case, the dynamic error of the measurement MSE can be reduced significantly compared to the optimal constant time of the measurement. This methodology of signal filtration was applied in the simulation model and the results of simulation were compared with field measurements taken with the use of a conventional radiometric density meter.
Paper VI111-09.5 
PDF · Video · Robust Hoo Estimation of Retarded State-Multiplicative Systems

Gershon, EliTel Aviv Univ
Keywords: Filtering and smoothing, Estimation and filtering, Synthesis of stochastic systems
Abstract: Linear, discrete-time systems with state-multiplicative noise and delayed states are considered. The problem of robust Hoo general-type filtering is solved for these systems when the uncertainty in their deterministic parameters is of the polytopic-type. The obtained vertex-dependant solution is based on a modified Finsler lemma which leads to a simple set of LMIs condition. The included numerical example demonstrates the tractability and solvability of the proposed method.
Paper VI111-09.6 
PDF · Video · Performance Assessment and Design of Quadratic Alarm Filters

Roohi, MohammadUniversity of Alberta
Chen, TongwenUniversity of Alberta
Keywords: Filtering and smoothing, Fault detection and diagnosis
Abstract: Alarm filtering is a structurally simple, easy to implement, and effective method to improve industrial alarm systems. Owing to these advantages, alarm filters are widely used in industrial applications. Linear and quadratic are the main types of alarm filters. Although a linear filter can detect mean changes, it can not be used to detect variation changes. However, a quadratic filter can be used to detect both types of changes. Although this remarkable feature of quadratic filters has been addressed in the literature, no explicit performance analysis is performed yet. So, deriving an analytical solution for quadratic filters is of paramount importance. To this aim, we propose an analytical method for performance assessment and design of quadratic filters. On the other side, in industrial applications, many process variables are acquired. So one challenge is to identify the process variable that provides the best alarm performance after filtering. We will derive an analytical solution to this problem. Furthermore, we will prove that this optimal solution is a function of the statistical feature of historical data and alarm filter structure.
Paper VI111-09.7 
PDF · Video · Algorithms for Integrated Processing of Marine Gravimeter Data and GNSS Measurements

Stepanov, O.A.Concern CSRI Elektropribor, JSC; University ITMO
Koshaev, DmitryConcern CSRI Elektropribor, JSC; University ITMO
Motorin, Andrei V.Concern CSRI Elektropribor, JSC; University ITMO
Krasnov, AntonConcern CSRI Elektropribor, JSC; University ITMO
Sokolov, AlexanderConcern CSRI Elektropribor, JSC; University ITMO
Keywords: Filtering and smoothing, Nonlinear system identification, Software for system identification
Abstract: Efficiency of using global navigation satellite system (GNSS) measurements for determining gravity anomalies (GA) at sea by solving filtering and smoothing problems based on GNSS and gravimeter data is studied. The GA, ship heaving, errors of GNSS and gravimeter measurements are presented as stochastic processes. The analysis is based on the standard deviations of the GA estimation errors, calculated at different heaving parameters and in different modes of GNSS data processing.
Paper VI111-09.8 
PDF · Video · Granger Causality of Gaussian Signals from Noisy or Filtered Measurements

Ahmadi, SalmanUniversity of Melbourne
Nair, Girish N.University of Melbourne
Weyer, ErikUniversity of Melbourne
Keywords: Time series modelling
Abstract: This paper investigates the assessment of Granger causality (GC) between jointly Gaussian signals based on noisy or filtered measurements. To do so, a recent rank condition for inferring GC between jointly Gaussian stochastic processes is exploited. Sufficient conditions are derived under which GC can be reliably inferred from the second order moments of the noisy or filtered measurements. This approach does not require a model of the underlying Gaussian system to be identified. The noise signals are not required to be Gaussian or independent, and the filters may be noncausal or nonminimum-phase, as long as they are stable.
Paper VI111-09.9 
PDF · Video · Sparse Representation of Feedback Filters in Delta-Sigma Modulators

Nagahara, MasaakiThe University of Kitakyushu
Yamamoto, YutakaKyoto Univ
Keywords: Filtering and smoothing, Quantized systems, Networked embedded control systems
Abstract: In this paper, we propose sparse representation of FIR (Finite Impulse Response) feedback filters in delta-sigma modulators. The filter has a sparse structure, that is, only a few coefficients are non-zero, that stabilizes the feedback modulator, and minimizes the maximum magnitude of the noise transfer function at low frequencies. The optimization is described as an L1 minimization with linear matrix inequalities (LMIs), based on the generalized KYP (Kalman-Yakubovich-Popov) lemma. A design example is shown to illustrate the effectiveness of the proposed method.
Paper VI111-09.10 
PDF · Video · Sparse ℓ1 and ℓ2 Center Classifiers

Calafiore, GiuseppePolitecnico Di Torino
Fracastoro, GiuliaPolitecnico Di Torino
Keywords: Machine learning
Abstract: The nearest-centroid classifier is a simple linear-time classifier based on computing the centroids of the data classes in the training phase, and then assigning a new datum to the class corresponding to its nearest centroid. Thanks to its very low computational cost, the nearest-centroid classifier is still widely used in machine learning, despite the development of many other more sophisticated classification methods. In this paper, we propose two sparse variants of the nearest-centroid classifier, based respectively on ℓ1 and ℓ2 distance criteria. The proposed sparse classifiers perform simultaneous classification and feature selection, by detecting the features that are most relevant for the classification purpose. We show that training of the proposed sparse models, with both distance criteria, can be performed exactly (i.e., the globally optimal set of features is selected) and at a quasi-linear computational cost. The experimental results show that the proposed methods are competitive in accuracy with state-of-the-art feature selection techniques, while having a significantly lower computational cost.
Paper VI111-09.11 
PDF · Video · Granger Causality Based Hierarchical Time Series Clustering for State Estimation

Tan, Sin YongIowa State University
Saha, HomagniIowa State University
Jacoby, MargariteUniversity of Colorado, Boulder
Florita, Anthony R.National Renewable Energy Laboratory
Henze, Gregor P.University of Colorado Boulder
Sarkar, SoumikIowa State University
Keywords: Time series modelling, Machine learning, Quantized systems
Abstract: Clustering is an unsupervised learning technique that is useful when working with a large volume of unlabeled data. Complex dynamical systems in real life often entail data streaming from a large number of sources. Although it is desirable to use all source variables to form accurate state estimates, it is often impractical due to large computational power requirements, and sufficiently robust algorithms to handle these cases are not common. We propose a hierarchical time series clustering technique based on symbolic dynamic filtering and Granger causality, which serves as a dimensionality reduction and noise-rejection tool. Our process forms a hierarchy of variables in the multivariate time series with clustering of relevant variables at each level, thus separating out noise and less relevant variables. A new distance metric based on Granger causality is proposed and used for the time series clustering, as well as validated on empirical data sets. Experimental results from occupancy detection and building temperature estimation tasks show fidelity to the empirical data sets while maintaining state-prediction accuracy with substantially reduced data dimensionality.
VI111-10
Estimation, Identification, and Discretization of Continuous-Time Systems Regular Session
Chair: Hirche, SandraTechnical University of Munich
Co-Chair: Oliveira, Vilma A.Universidade De Sao Paulo
Paper VI111-10.1 
PDF · Video · A Modified Non-Adaptive OSG-SOGI Filter for Estimation of a Biased Sinusoidal Signal with Global Convergence Properties

Fedele, GiuseppeUniversità Della Calabria
Pin, GilbertoUniversity of Padua
Parisini, ThomasImperial College & Univ. of Trieste
Keywords: Continuous time system estimation
Abstract: This paper presents an algorithm for estimating the parameters of a biased sinusoidal signal. The proposed method uses the output signals of a second order generalized integrator without adaptation on its resonant frequency to derive a linear regression equation where the unknown parameters are a nonlinear combination of bias and frequency of the input signal. The global stability of the method is proven. Remarkably, the proposed method represents the minimum-order estimator known for the problem under consideration, being implementable by a 4th-order adaptive system. Simulation results and comparisons with existing methods show the accurate estimation capability of the proposed approach.
Paper VI111-10.2 
PDF · Video · Identification of Continuous-Time Systems Utilising Kautz Basis Functions from Sampled-Data

Coronel Mendez, María de los AngelesUniversidad Técnica Federico Santa María
Carvajal, RodrigoUniversidad Tecnica Federico Santa Maria
Aguero, Juan C.Universidad Santa Maria
Keywords: Continuous time system estimation
Abstract: In this paper we address the problem of identifying a continuous-time deterministic system utilising sampled-data with instantaneous sampling. We develop an identification algorithm based on Maximum Likelihood. The exact discrete-time model is obtained for two cases: i) known continuous-time model structure and ii) using Kautz basis functions to approximate the continuous-time transfer function. The contribution of this paper is threefold: i) we show that, in general, the discretisation of continuous-time deterministic systems leads to several local optima in the likelihood function, phenomenon termed as aliasing, ii) we discretise Kautz basis functions and obtain a recursive algorithm for constructing their equivalent discrete-time transfer functions, and iii) we show that the utilisation of Kautz basis functions to approximate the true continuous-time deterministic system results in convex log-likelihood functions. We illustrate the benefits of our proposal via numerical examples.
Paper VI111-10.3 
PDF · Video · Adaptive Identification of Nonlinear Time-Delay Systems Using Output Measurements

Furtat, IgorInstitute of Problems of Mechanical Engineering Russian Academy
Orlov, YuryCICESE
Keywords: Continuous time system estimation
Abstract: A novel adaptive identifier is developed for nonlinear time-delay systems composed of linear, Lipschitz and non-Lipschitz components. To begin with, an identifier is designed for uncertain systems with a priori known delay values, and then it is generalized for systems with unknown delay values. The algorithm ensures the asymptotic parameter estimation and state observation by using gradient algorithms. The unknown delays and plant parameters are estimated by using a special equivalent extension of the plant equation. The algorithms stability is presented by solvability of linear matrix inequalities.
Paper VI111-10.4 
PDF · Video · Estimating the Membrane Properties of Vestibular Type II Hair Cells Using Continuous-Time System Identification

Pan, SiqiUniversity of Newcastle
Welsh, JamesUniversity of Newcastle
Brichta, AlanUniversity of Newcastle
Drury, HannahUniversity of Newcastle
Stoddard, Jeremy GrantUniversity of Newcastle
Keywords: Continuous time system estimation
Abstract: In this paper we apply a continuous-time system identification method, known as the Simplified Refined Instrumental Variable method for Continuous-time systems (SRIVC), to the problem of estimating membrane properties of vestibular Type II hair cells. Due to the non-ideal characteristics of the experimental system, additional parameters, other than those of the membrane are required to be estimated. The SRIVC algorithm is modified to allow known poles and zeros to be forced into the estimator. This modified algorithm is then applied to the identification of the membrane properties of vestibular Type II hair cells, yielding results commensurate with typically accepted values.
Paper VI111-10.5 
PDF · Video · Unknown System Dynamics Estimator for Nonlinear Uncertain Systems (I)

Yang, JunKunming University of Science and Technology
Na, JingUniversity of Bristol
Yu, HaoyongNational University of Singapore
Gao, GuanbinKunming University of Science & Technology
Wang, XiaodongKunming University of Science and Technology
Keywords: Continuous time system estimation, Bounded error identification, Identification for control
Abstract: For feedback control designs, one of the fundamental problems is to handle the unknown system dynamics. In this paper, an alternative unknown system dynamics estimator (USDE) with low-pass filter operations is presented based on an invariant manifold method, in which we only need to set a scalar, the filter parameter. The convergence performance and robustness of this USDE are analysed in both the time-domain and frequency-domain. To circumvent the sensitiveness to the measurement noise, a further enhanced USDE (EUSDE) with two-layer of low-pass filters is constructed. With the proposed estimators, all time-varying components, such as unmodeled dynamics, nonlinearities and external disturbances, can be viewed as a lumped unknown system dynamics term and then effectively estimated even in the presence to fair measurement noise. The function of these estimators is the same as the well-known disturbance observer (DOB) and extended state observer (ESO). Hence, they can be easily incorporated into control schemes. Numerical simulation results are presented to show the effectiveness of the proposed estimation schemes.
Paper VI111-10.6 
PDF · Video · Nonlinear Observer Design for Systems with Sampled Measurements: An LPV Approach

Boukal, YassineUniversité Polytechnique Hauts-De-France,
Zerrougui, MohamedAix Marseille University
Zemouche, AliCRAN UMR CNRS 7039, University of Lorraine
Outbib, RachidUniversity of Aix-Marseille - LIS
Keywords: Continuous time system estimation, Estimation and filtering
Abstract: The aim of this work is to propose a design methodology of observers for a class of Lipschitz nonlinear dynamical systems with sampled measurements by using the differential mean value theorem (DMVT) which allows us to transform the nonlinear part of the estimation error dynamics into a linear parameter varying (LPV) system. The designed observer must ensure the stability of the estimation error subject to a sampled measurements. An LMI-based minimization problem is provided to ensure the stability and the existence of the observer using Lyapunov theory. Thus, the measurements sampling period is included in the LMI as a decision parameter. Indeed, this allows to widen the sampling period as much as possible, which helps optimization of energy consumption while guaranteeing the convergence of the observer. Finally, to illustrate the performance of the proposed methodology, a numerical example is presented.
Paper VI111-10.7 
PDF · Video · Enforcing Stability through Ellipsoidal Inner Approximations in the Indirect Approach for Continuous-Time System Identification

González, Rodrigo A.KTH Royal Institute of Technology
Welsh, JamesUniversity of Newcastle
Rojas, Cristian R.KTH Royal Institute of Technology
Keywords: Continuous time system estimation, Estimation and filtering, Stochastic system identification
Abstract: Recently, a new indirect approach method for continuous-time system identification has been proposed that provides complete freedom on the number of poles and zeros of the linear and time-invariant continuous-time model structure. However, this procedure has reliability issues, as it may deliver unstable estimates even if the initialisation model and true system are stable. In this paper, we propose a method to overcome this problem. By generating ellipsoids that contain parameter vectors whose coefficients yield stable polynomials, we introduce a convex constraint in the indirect prediction error method formulation, and show that the proposed method enjoys optimal asymptotic properties while being robust in small and noisy data set scenarios. The effectiveness of the novel method is tested through extensive simulations.
Paper VI111-10.8 
PDF · Video · Analysis of the Parameter Estimate Error When Algebraic Differentiators Are Used in the Presence of Disturbances

Othmane, AmineUniversité Paris-Saclay; Saarland University
Rudolph, JoachimSaarland University
Mounier, HuguesCNRS SUPELEC Université Paris
Keywords: Continuous time system estimation, Filtering and smoothing
Abstract: The use of algebraic differentiators in the context of asymptotic continuous-time parameter estimation is discussed. The estimation problem is analyzed within a least squares optimization context. Bounds for the error stemming from high frequency disturbances and the approximation of the derivatives are derived. It is shown that with higher frequencies the error stemming from the disturbances decreases and that the filter parameters can be used to adjust the convergence of this error to zero. An observer with assignable error dynamics for the online estimation is also proposed. A simulation is carried out to evaluate the results and compare the proposed observer with the recursive solution of the least squares problem.
Paper VI111-10.9 
PDF · Video · State Estimation for a Locally Unobservable Parameter-Varying System: One Gradient-Based and One Switched Solutions

Aranovskiy, StanislavCentraleSupelec - IETR
Efimov, DenisInria
Sokolov, DmitryUniversité De Lorraine
Wang, JianHangzhou Dianzi University
Ryadchikov, IgorKuban State University
Bobtsov, AlexeyITMO University
Keywords: Continuous time system estimation, Mechanical and aerospace estimation
Abstract: This work is motivated by a case study of a mechanical system where a sensor bias yields loose of observability for certain values of time-varying parameters. Two solutions are proposed: a nonlinear gradient-based observer that requires the persistency of excitation of the system trajectories and a switched observer that imposes an average dwell-time requirement. For both observers, asymptotic convergence of the estimates is proven. The theoretical results are supported by illustrative numerical simulations.
Paper VI111-10.10 
PDF · Video · Finite-Time Frequency Estimator for Harmonic Signal

Bobtsov, AlexeyITMO University
Vediakova, AnastasiiaSaint Petersburg State University
Nikolaev, NikolayITMO University
Slita, OlgaITMO University
Pyrkin, AntonITMO University
Vedyakov, AlexeyITMO University
Keywords: Continuous time system estimation, Nonlinear system identification
Abstract: This paper is devoted to a frequency estimation of a pure sinusoidal signal in finite-time. The parameterization is based on applying delay operators to a measurable signal. The result is the first-order linear regression model with one parameter, which depends on the signal frequency. The proposed method of finite-time estimation consists of two steps. On the first step, the standard gradient descent method is used to estimate the regression model parameter. On the next step using algebraic equations, finite-time frequency estimate is found. The described method does not require measuring or calculating derivatives of the input signal and uses one integrator for the gradient method and another one for the finite-time estimation. The efficiency of the proposed approach is demonstrated through the set of numerical simulations.
Paper VI111-10.11 
PDF · Video · Coefficients and Delay Estimation of the General Form of Fractional Order Systems Using Non-Ideal Step Inputs

Hashemniya, FatemehFaculty of Electrical Engineering, K. N. Toosi University of Tec
Tavakoli-Kakhki, MahsanK.N. Toosi University of Technology
Azarmi, RoohallahEindhoven University of Technology
Keywords: Continuous time system estimation, Recursive identification, Identification for control
Abstract: This paper proposes a novel method for the simultaneous estimation of the coefficients and the delay term of a delayed fractional order system. Because of the practicality aspect of the non-ideal step inputs, such inputs are used in this paper for the first time to identify a fractional order system. To this end, the proposed identification procedure is separately described for two types of fractional order systems, i.e., including both non-delayed and delayed systems. For the non-delayed system, a fractional order integral approach is developed, and for the delayed system, a filtering approach is investigated to make the delay term to be explicitly appeared in the parameters vector. In simulation results, some illustrative examples, covering both non-delayed and delayed systems, are given to demonstrate the validity of the proposed method.
Paper VI111-10.12 
PDF · Video · Using Multivariate Polynomials to Obtain DC-DC Converter Voltage Gain

Magossi, Rafael Fernando QuirinoCentro Federal De Educação Tecnológica Celso Suckow Da Fonseca,
Fuzato, GuilhermeFederal Institute of Education, Science and Technology of São Pa
Silva de Castro, DanielUniversity of São Paulo
Quadros Machado, RicardoUSP
Oliveira, Vilma A.Universidade De Sao Paulo
Keywords: Experiment design, Grey box modelling, Continuous time system estimation
Abstract: In this paper, a data driven approach is used to obtain the static gain of dc--dc power converters in terms of the duty cycle and a set of linear coefficients. A known number of measurements, dependent on the dc--dc converter topology, are used to built-in a rational function obtained by linear coefficients. This solution shows how to use measurements to determine a function to represent the static gain of dc--dc power converters in the continuous-conduction mode (CCM). To validate the proposed approach, PSIM simulations, as well as experimental results are presented. The analysis was performed with a Interleaved Boost with Voltage Multiplier (IBVM) converter. Finally, the proposed approach is shown to be an alternative to the classical scanning methods or to the conventional solution of differential equations.
Paper VI111-10.13 
PDF · Video · Parameter Estimation in Input Matrix under Gain Constraints in Specified Frequency Ranges

Sato, MasayukiJapan Aerospace Exploration Agency
Keywords: Grey box modelling, Frequency domain identification, Continuous time system estimation
Abstract: This paper addresses parameter estimation problem of Continuous-/Discrete-Time (CT/DT) Linear Time-Invariant (LTI) systems, whose gain properties should satisfy given constraints in a priori specified frequencies, using measured data. The following are supposed in our problem: i) only input matrix has parameters to be estimated; ii) the state and the input are both measured, and the derivative of the state is also measured in CT case, and iii) the gain constraints in specified frequency ranges are given beforehand. Under these suppositions, a formulation to minimize the difference between the measured state derivative and the expected state derivative (in CT case) or the difference between the measured one-step-ahead state and the expected one-step-ahead state (in DT case) in Euclidean norm with the supposed gain constraints satisfied is given in terms of Linear Matrix Inequality (LMI). The effectiveness of the proposed method is demonstrated by an academic example in DT case as well as flight data obtained by JAXA's airplane in CT case.
Paper VI111-10.14 
PDF · Video · Minimum Phase Properties of Systems with a New Signal Reconstruction Method

Ou, MinghuiChongqing University
Liang, ShanChongqing University
Zhang, HaoCollege of Automation, Chongqing University
Liu, TongChongqing University
Liang, JingCollege of Automation,chongqing University
Keywords: Input and excitation design, Continuous time system estimation, Stability and stabilization of hybrid systems
Abstract: The Minimum Phase (MP) properties of linear control systems can be reflected by its zero stability. The stability of zeros affects the system control performance. When a continuous-time system is discretized to a discrete-time system, the discretization process may render continuous-time system models have nonminimum phase. This paper analyses the MP properties of system and deduces a new stable condition of the zeros when continuous-time system is discretized by Forward Triangle Sample and Hold (FTSH) for sufficiently small sampling periods. Finally, two numerical examples have verified our results.
Paper VI111-10.15 
PDF · Video · Machine Learning for Receding Horizon Observer Design: Application to Traffic Density Estimation

Georges, DidierGrenoble Institute of Engineering and Management - Univ. Grenobl
Keywords: Machine learning, Continuous time system estimation
Abstract: This paper is devoted to the application of a simple machine learning technique for the design of a receding horizon state observer. The proposed approach is based on a neural network trained to learn the inverse problem consisting in deriving the current system state from past measurements and inputs. The training data is obtained from simple integrations of the system dynamics to be observed. The approach is here applied to the problem of estimating the car density on a highway online. A comparison with the solution of an receding horizon observer based on an adjoint method and used as reference demonstrates the effectiveness of the proposed approach.
Paper VI111-10.16 
PDF · Video · Robust Sampling Time Designs for Parametric Uncertain Systems

Wang, KeUniversity of Strathclyde
Yue, HongUniversity of Strathclyde
Keywords: Model formulation, experiment design, Identification and validation, Developments in measurement, signal processing
Abstract: Robust experimental design (RED) of sampling time scheduling has been discussed for parametric uncertain systems. Four RED methods, i.e., the pseudo-Bayesian design, the maximin design, the expectation-variance design, and the online experimental redesign, are investigated under the framework of model-based optimal experimental design (OED). Both the D-optimal and the E-optimal criteria are used as performance metrics. Two numerical procedures, the Powell's method and the semi-definite programming (SDP), are employed to obtain the optimum solution for REDs. The robustness performance of the four REDs are compared using a benchmark enzyme reaction system. In comparison to a typical uniform sampling strategy, the sampling time profiles from REDs are more focused on regions where the dynamic system has higher parametric sensitivities, indicating choice of informative data for parameter identification. The designed sampling strategies are also assessed by bootstrap parameter estimation with randomly generated initial points, where the difference between REDs can be observed.
Paper VI111-10.17 
PDF · Video · Consistent Discretization of a Class of Predefined-Time Stable Systems

Jiménez-Rodríguez, EstebanCINVESTAV - Unidad Guadalajara
Aldana-López, RodrigoUniversidad De Zaragoza
Sanchez-Torres, Juan DiegoITESO
Gómez-Gutiérrez, DavidIntel Coporation
Loukianov, Alexander G.Cinvestav Ipn Gdl
Keywords: Digital implementation, Stability of nonlinear systems, Application of nonlinear analysis and design
Abstract: As the main contribution, this document provides a consistent discretization of a class of fixed-time stable systems, namely predefined-time stable systems. In the unperturbed case, the proposed approach allows obtaining not only a consistent but exact discretization of the considered class of predefined-time stable systems, whereas in the perturbed case, the consistent discretization preserves the predefined-time stability property. All the results are validated through simulations and compared with the conventional explicit Euler scheme, highlighting the advantages of this proposal.
VI111-11
Fault Detection and Diagnosis Regular Session
Chair: Patton, Ron J.Univ. of Hull
Co-Chair: Ding, Steven X.Univ of Duisburg-Essen
Paper VI111-11.1 
PDF · Video · Multiple Multiplicative Actuator Fault Detectability Analysis Based on Invariant Sets for Discrete-Time LPV Systems

Min, BoTsinghua University
Xu, FengTsinghua Univerisity
Tan, JunboTsinghua University
Wang, XueqianTsinghua University
Liang, BinTsinghua University
Keywords: Fault detection and diagnosis
Abstract: This paper proposes a generalized minimum detectable fault (MDF) computation method based on the set-separation condition between the healthy and faulty residual sets for discrete-time linear parameter varying (LPV) systems with bounded inputs and uncertainties. First, we equivalently transform the multiple multiplicative actuator faults into the form of multiple additive actuator faults, which is bene cial to simplify the problem. Then, by considering the 1-norm of the fault vector, we defi ne the generalized MDF in the case of multiple additive actuator faults, which can be computed via solving a simple linear programming (LP) problem. Moreover, an analysis of the effect of the input vector on the magnitude of the generalized MDF is made. Since the proposed generalized MDF computation method is robust by considering the bounds of inputs and uncertainties, robust fault detection (FD) can be guaranteed whenever the sum of the magnitudes of all occurred faults is larger than the magnitude of the generalized MDF. At the end of this paper, a numerical example is used to illustrate the effectiveness of the proposed method.
Paper VI111-11.2 
PDF · Video · On Real-Time Fatigue Damage Prediction for Steam Turbine

Xu, BoUniversity of Jinan
Sun, YongjianUniversity of Jinan
Keywords: Fault detection and diagnosis
Abstract: This paper presents a real-time prediction method for fatigue damage of steam turbine. The temperature data and thermal stress data of the key parts are extracted by calculating the temperature field and the stress field. The composite stress is calculated according to the fourth strength theory, and the measured stress data are normalized. Support vector regression model is established, input and output data are trained and predicted. The relationship between stress and damage function is analyzed and fitted, and the framework of the real time fatigue damage prediction system is established. In the end, the effectiveness of the method is verified by simulation experiment.
Paper VI111-11.3 
PDF · Video · Probabilistic Robust Parity Relation Based Fault Detection Using Biased Minimax Probability Machine

Ma, YujiaHuazhong University of Science and Technology
Wan, YimingHuazhong University of Science and Technology
Zhong, MaiyingShandong University of Science and Technology
Keywords: Fault detection and diagnosis
Abstract: This paper proposes a probabilistic robust parity relation based approach to fault detection of stochastic linear systems. Instead of assuming exact knowledge of disturbance distribution, the uncertainty of distribution information is taken into account by considering an ambiguity set of disturbance distributions. The biased minimax probability machine scheme is exploited to formulate an integrated design of the parity vector/matrix and the detection threshold. It maximizes the worst-case fault detection rate (FDR) with respect to selected reference faults, while ensuring a predefined worst-case false alarm rate. Firstly, a scalar residual design is derived in an analytical form. The analysis of its FDR in the presence of an arbitrary fault shows its limitation due to using a single reference fault. This issue is further addressed by proposing a vector residual design with a systematic method to select multiple reference faults. The efficacy of the proposed approach is illustrated by a simulation example.
Paper VI111-11.4 
PDF · Video · Distributionally Robust Fault Detection by Using Kernel Density Estimation

Xue, TingUniversity of Duisburg-Essen
Zhong, MaiyingShandong University of Science and Technology
Luo, LijiaZhejiang University of Technology
Li, LinlinUniversity of Science and Technology Beijing
Ding, Steven X.Univ of Duisburg-Essen
Keywords: Fault detection and diagnosis
Abstract: In this paper, a method of distributionally robust fault detection (FD) is proposed for stochastic linear discrete-time systems by using the kernel density estimation (KDE) technique. For this purpose, an H2 optimization-based fault detection filter is constructed for residual generation. Towards maximizing the fault detection rate (FDR) for a prescribed false alarm rate (FAR), the residual evaluation issue regarding the design of residual evaluation function and threshold is formulated as a distributionally robust optimization problem, wherein the so-called confidence sets are constituted to model the ambiguity of distribution knowledge of residuals in fault-free and faulty cases. A KDE based solution, robust to the estimation errors in probability distribution of residual caused by the finite number of samples, is further developed to address the targeting problem such that the residual evaluation function, threshold as well as the lower bound of FDR can be achieved simultaneously. A case study on a vehicle lateral control system demonstrates the applicability of the proposed FD method.
Paper VI111-11.5 
PDF · Video · Fault Detection and Identification for Nonlinear MIMO Systems Using Derivative Estimation

Lomakin, AlexanderUniversität Erlangen-Nürnberg
Deutscher, JoachimUniversität Ulm
Keywords: Fault detection and diagnosis
Abstract: In this paper a method for fault detection and identification of affine input nonlinear systems is presented, which is based on derivative estimation with orthonormal Jacobi polynomials. A systematic approach is presented to derive a residual and a differential algebraic expression of the fault from the system description, which solely depends on measurable input and output signals as well as on their time derivatives. For this, a systematic algorithm is provided, which can be directly implemented in computer algebra packages. Furthermore, arbitrary disturbances are taken into account, by making use of a disturbance decoupling. Fault detection and identification is then achieved by polynomial approximation of the determined fault or residual expression. The results are illustrated for a faulty point-mass satellite model.
Paper VI111-11.6 
PDF · Video · Robust Anomaly Detection Based on a Dynamical Observer for Continuous Linear Roesser Systems

Alikhani, HamidK.N. Toosi University of Technology
Meskin, NaderQatar University
Aliyari Shoorehdeli, MahdiK.N. Toosi University of Technology
Keywords: Fault detection and diagnosis
Abstract: Monitoring of industrial systems for anomalies such as faults and cyber-attacks as unknown and extremely undesirable inputs in the presence of other inputs (like disturbances) is an important issue for ensuring the safety and the reliability of their operation. In this study, a robust anomaly detection filter is proposed for continuous linear Roesser systems using dynamic observer framework. Sufficient conditions for the existence of the observer and its sensitivity to anomaly as well as its robustness to disturbances are addressed via linear matrix inequalities (LMIs). The mentioned sensitivity and robustness are based on the H_- and H_infty performance indices, respectively. Finally, the performance of the proposed observer is demonstrated through a numerical example.
Paper VI111-11.7 
PDF · Video · A Novel Probabilistic Fault Detection Scheme with Adjustable Reliability Estimates

Wang, ChangrenTsinghua University
Shang, ChaoTsinghua University
Huang, DexianTsinghua University
Yu, BinHengli Petrochemical Co., Ltd
Keywords: Fault detection and diagnosis
Abstract: We propose a novel probabilistic fault detection scheme with adjustable reliability estimates. Our scheme consists of two phase, the first is the modelling phase, where a probabilistic fault detection design is devised, while the second is the validation phase, where reliability estimates of the design are adjusted online according to new operation records of the plant and the validated reliability. The modelling phase is based on two methods: residual generation, such as parity space, which is an important tool in fault detection problem, and scenario approach, which is a seminal trick to transfer intractable optimization problem into approximate tractable optimization problem and ensure reliability guarantees. The validation phase leverages the state-of-art posteriori probabilistic bounds of convex scenario programs with validation tests. Such a holistic design-and-validate scheme will can help technicians to make better decision. The efficacy of the proposed approach is illustrated on a simulated case study
Paper VI111-11.8 
PDF · Video · A Model-Based Fault-Detection Strategy in DC/AC Conversion

Pyrkin, AntonITMO University
Cisneros, RafaelFREEDM-NCSU
Campos-Delgado, Daniel U.UASLP
Bobtsov, AlexeyITMO University
Somov, SergeyITMO University
Keywords: Fault detection and diagnosis, Adaptive observer design, Estimation and filtering
Abstract: An open-circuit fault-detection strategy is here proposed for single-phase DC/AC conversion. The power converter under consideration consists of an H-bridge and a capacitor with parallel resistance and current source in its DC side-these last two stand for the unknown system load and energy injection from renewable resources, respectively. An inductor filter is also included as a coupling element to the AC network. When an open-circuit fault occurs in the H-bridge, the resulting AC output waveform is asymmetric, and induces DC and harmonic components to the network. Hence, by using an additive fault modeling, the fault signature can be expressed by a constant term f_dc and a fluctuating signal. The sign of f_dc allows to determine the pair of faulty switches in the H-bridge. In this work, an DREM-based identification scheme is proposed to estimate f_dc. Through the sign of its estimate, it is possible to detect the pair of faulty switches. To assess our approaches, simulation results are included.
Paper VI111-11.9 
PDF · Video · Robust Actuator Fault Diagnosis Algorithm for Autonomous Hexacopter UAVs

González Rot, AntonioSouthern Denmark University
Hasan, AgusUniversity of Southern Denmark
Manoonpong, PoramateUniversity of Southern Denmark
Keywords: Fault detection and diagnosis, Adaptive observer design, Mechanical and aerospace estimation
Abstract: This paper presents a robust actuator fault diagnosis algorithm for hexacopter Unmanned Aerial Vehicles (UAVs). The algorithm, based on Adaptive eXogenous Kalman Filter (AXKF), consists of two-stage operations: (i) a nonlinear observer and (ii) a linearized adaptive Kalman filter. To this end, we provide a sufficient condition for the nonlinear observer and recursive formulas for the linearized adaptive Kalman filter. The algorithm is tested for actuator fault diagnosis of a hexacopter UAV. Simulation results show that the proposed cascaded algorithm is able to accurately estimate the magnitude of the actuator fault.
Paper VI111-11.10 
PDF · Video · Distributed H−/L∞ Fault Detection Observer Design for Linear Systems

Han, WeixinNorthwestern Polytechnical University
Trentelman, Harry L.Univ. of Groningen
Xu, BinNanyang Technological University
Keywords: Fault detection and diagnosis, Distributed control and estimation, Sensor networks
Abstract: This paper studies the distributed fault detection problem for linear time-invariant (LTI) systems with distributed measurement output. A distributed H−/L∞ fault detection observer (DFDO) design method is proposed to detect actuator faults of the monitored system in the presence of a bounded process disturbances. The DFDO consists of a network of local fault detection observers, which communicate with their neighbors as prescribed by a given network graph. By using finite-frequency H− performance, the residual in fault detection is sensitive to fault in the interested frequency-domain. The residual is robust against effects of the external process disturbance by L∞ analysis. A systematic algorithm for DFDO design is addressed and the residual thresholds are calculated in our distributed fault detection scheme. Finally, we use a numerical simulation to demonstrate the effectiveness of the proposed distributed fault detection approach.
Paper VI111-11.11 
PDF · Video · Intermittent Fault Detection for Nonlinear Stochastic Systems

Niu, YichunChina University of Petroleum
Sheng, LiChina University of Petroleum (East China)
Gao, MingChina University of Petroleum (East China)
Zhou, DonghuaShandong Univ. of Science and Technology
Keywords: Fault detection and diagnosis, Estimation and filtering
Abstract: In this paper, the problem of intermittent fault detection is investigated for nonlinear stochastic systems. The moving horizon estimation with dynamic weight matrices is proposed, where the weight matrices are adjusted by an unreliability index of prior estimate to avoid the smearing effects of intermittent faults. Based on the particle swarm optimization algorithm, the nonlinear optimization problem is solved and the approximate estimate is derived. Finally, the feasibility and effectiveness of the proposed algorithm are validated by a numerical example.
Paper VI111-11.12 
PDF · Video · Asymmetrical Load Mitigation of Wind Turbine Pitch Actuator Faults Using Unknown Input-Based Fault-Tolerant Control (I)

Liu, YanhuaUniversity of Hull
Patton, Ron J.Univ. of Hull
Shi, ShuoUniversity of Hull
Keywords: Fault detection and diagnosis, Estimation and filtering
Abstract: Offshore wind turbines suffer from asymmetrical blade loading, resulting in enhanced structural fatigue. Individual pitch control (IPC) is an effective method to achieve blade load mitigation, accompanied by enhancing the pitch movements and thus increased the probability of pitch actuator faults. The occurrence of faults will deteriorate the IPC load mitigation performance, which requires fault-tolerant control (FTC). IPC is itself analogous to the FTC problem because the action of rotor bending can be considered as a fault effect. Therefore, the work thus proposes a "co-design" strategy, constituting a combination of IPC-based asymmetrical load mitigation combined with FTC acting at the pitch system level. The FTC uses the well-known fault estimation and compensation strategy. A Proportional-Integral PI-based IPC strategy for blade mitigation is proposed in which the robust fault estimation is achieved using a robust unknown input observer (UIO). The performance of two pitch controllers (baseline pitch controller, PI-based IPC) are compared in the presence of pitch actuator faults (including low pressure & loss of effectiveness). The effectiveness of the proposed strategy is verified on the 5MW NREL wind turbine system.
Paper VI111-11.13 
PDF · Video · Damage Identification for the Tree-Like Network through Frequency-Domain Modeling

Ni, XiangyuUniversity of Notre Dame
Goodwine, BillUniversity of Notre Dame
Keywords: Fault detection and diagnosis, Frequency domain identification, Multi-agent systems
Abstract: In this paper, we propose a method to identify the damaged component and quantify its damage amount in a large network given its overall frequency response. The identification procedure takes advantage of our previous work which exactly models the frequency response of that large network when it is damaged. As a result, the test shows that our method works well when some noise present in the frequency response measurement. In addition, the effects brought by a damaged component which is located deep inside that large network are also discussed.
Paper VI111-11.14 
PDF · Video · A Sensor-To-Sensor Model-Based Change Detection Approach for Quadcopters

Ho, DuLinköping University
Hendeby, GustafLinköpings Universitet
Enqvist, MartinLinköping University
Keywords: Fault detection and diagnosis, Grey box modelling, Channel estimation/equalisation
Abstract: This paper addresses the problem of change detection for a quadcopter in the presence of wind disturbances. Different aspects of the quadcopter dynamics and various flight conditions have been investigated. First, the wind is modeled using the Dryden wind model as a sum of a low-frequent and a turbulent part. Since the closed-loop control can compensate for system changes and disturbances and the effect of the wind disturbance is significant, the residuals obtained from a standard simulation model can be misleading. Instead, a sensor-to-sensor submodel of the quadcopter is selected to detect a change in the payload using the Instrumental Variables (IV) cost function. It is shown that the mass variation can be detected using the IV cost function in different flight scenarios.
Paper VI111-11.15 
PDF · Video · On the Choice of Multiple Flat Outputs for Fault Detection and Isolation of a Flat System

Rammal, RimUniversity of Bordeaux
Airimitoaie, Tudor-BogdanUniv. Bordeaux
Cazaurang, FranckUniv. Bordeaux I
Levine, JeanEcole Des Mines, CAS
Melchior, PierreUniversité De Bordeaux - Bordeaux INP/ENSEIRB-MATMECA
Keywords: Fault detection and diagnosis, Identifiability, Filtering and smoothing
Abstract: This paper presents a rigorous definition of the isolability of a fault in a flat system whose flat outputs are measured by sensors that are subject to faults. In particular, if only one sensor or actuator is faulty at a time, we show that the isolation of faults can be achieved if a pair of flat outputs satisfies some independence condition. A detailed characterization of this condition is presented. Finally, the pertinence of the isolability concept is demonstrated on the example of a three tank system.
Paper VI111-11.16 
PDF · Video · Robust Fault Detection and Isolation of Discrete-Time LPV Systems Combining Set-Theoretic UIO and Invariant Sets

Tan, JunboTsinghua University
Xu, FengTsinghua Univerisity
Yang, JunTsinghua University
Wang, XueqianTsinghua University
Liang, BinTsinghua University
Keywords: Fault detection and diagnosis, LPV system identification, Stability and stabilization of hybrid systems
Abstract: This paper proposes a mixed active/passive robust fault detection and isolation (FDI) method for discrete-time linear paramter varying (LPV) systems based on set-theoretic unknown input observers (SUIO) and invariant sets. The robustness against system uncertainties (i.e., process disturbances, measurement noises and so on) in FDI of LPV systems can be guaranteed by actively decoupling or passively bounding their effect on residual signal. Furthermore, the quadratic H1 stability condition of the LPV-form state-estimation-error dynamics is established based on a group of linear matrix inequalities (LMIs). Under the precondition of stability, a family of residual sets are constructed to establish set-separation guaranteed fault isolation (FI) conditions using invariant sets off-line. As long as the occurred faults satisfy the guaranteed FI conditions, they can be isolated from each other. At the end, a numerical example is used to illustrate the effectiveness of the proposed method.
Paper VI111-11.17 
PDF · Video · Improved Process Diagnosis Using Fault Contribution Plots from Sparse Autoencoders

Hallgrímsson, ásgeir DanielTechnical University of Denmark
Niemann, HenrikTechnical University of Denmark
Lind, MortenTechnical University of Denmark
Keywords: Fault detection and diagnosis, Machine learning, Grey box modelling
Abstract: Development of model-based fault diagnosis methods is a challenge when industrial systems are large and exhibit complex process behavior. Latent projection (LP), a statistical method that extract features of data via dimensionality reduction, is an alternative approach to diagnosis as it can be formulated to not rely on process knowledge. However, LP methods may perform poorly at identifying abnormal process variables due a "fault smearing" effect - variables unaffected by a fault are unintentionally characterized as being abnormal. The effect occurs because data compression permits faulty and non-faulty variables to interact. This paper presents an autoencoder (AE), a nonlinear LP method based on neural networks, as a monitoring method of a simulated nonlinear triple tank process (TTP). Simulated process data was used to train the AE to generate a monitoring statistic representing the condition of the TTP. Sparsity was introduced in the AE to reduce variable interactivity. The AE's ability to detect a fault was demonstrated. The individual contributions of process variables to the AE's monitoring statistic were analyzed to reveal the process variables that were no longer consistent with normal operating conditions. The key result in this study was that sparsity reduced fault smearing onto unaffected variables and increased the contributions of actual faulty variables.
Paper VI111-11.18 
PDF · Video · Tension Monitoring of Toothed Belt Drives Using Interval-Based Spectral Features

Fehsenfeld, MoritzLeibniz University Hannover
Johannes, KühnLenze Automation GmbH
Wielitzka, MarkLeibniz University Hanover
Ortmaier, TobiasGottfried Wilhelm Leibniz Universität Hannover
Keywords: Fault detection and diagnosis, Machine learning, Time series modelling
Abstract: Toothed belt drives are used in manifold automation applications. But only if the belt tension is properly adjusted, optimal working conditions are ensured. A loss of efficiency or even breakdowns might be the consequences otherwise. For this reason, tension monitoring reduces operation costs and may prevent failures. In order to meet industrial requirements, the monitoring is supposed to rely on standard sensor data. From this data, features are extracted in time and frequency domain which are passed on to a random forest. For further improvement, a segmentation of the frequency spectrum is performed beforehand. In this way, interval-based spectral features can be extracted to capture small distinctive parts in the frequency domain. For this purpose, two different segmentation procedures are compared in a random forest regression. A belt drive powered by a 1.9 kW synchronous servomotor is used to evaluate the proposed approaches in two different industrial scenarios. The experimental results show that both segmentation methods enhance the performance of a tree-based regression and offer a reliable tension prediction.
Paper VI111-11.19 
PDF · Video · Actuation Failure Detection in Fixed-Wing Aircraft Combining a Pair of Two-Stage Kalman Filters

de Angelis Cordeiro, RafaelInstituto Superior Técnico
Azinheira, José RaúlInstituto Superior Técnico - Technical Univ of Lisbon
Moutinho, AlexandraIDMEC/LAETA, Instituto Superior Técnico, Universidade De Lisboa
Keywords: Fault detection and diagnosis, Mechanical and aerospace estimation
Abstract: Actuation failure is one of the causes of loss of control in-flight accidents. Aircraft usually have multiple redundant actuators to mitigate failures, and Failure Detection and Isolation Systems (FDIS) are used to diagnose failures and reconfigure software/hardware to enhance safety. However, the large number of redundant actuators interferes with the FDIS. To detect and isolate failures in fixed-wing aircraft with redundant actuators, this work proposes the combined use of two different strategies of the Two-Stage Kalman Filter. A Supervisory Loop is included using heuristics and statistics to diagnose the actuators, and a Feed-Forward Differential is implemented to improve the isolation process without interfering with the aircraft flight. The solution is evaluated in the detection of an aileron failure in a Boeing 747 simulator.
Paper VI111-11.20 
PDF · Video · Sensor Fault Identification in Nonlinear Dynamic Systems

Zhirabok, Alexey N.Far Eastern Federal Univ
Zuev, AlexanderFar Eastern Federal University
Shumsky, AlexeyFar Eastern Federal University
Keywords: Fault detection and diagnosis, Nonlinear system identification
Abstract: The problem of sensor fault diagnosis in technical systems described by nonlinear dynamic models is considered. To address the problem, sliding mode observers are used. The suggested approach for constructing sliding mode observers is based on the reduced order model of the initial system. This allows to reduce complexity of sliding mode observers and relax the limitations imposed on the initial system.
Paper VI111-11.21 
PDF · Video · A Jump-Markov Regularized Particle Filter for the Estimation of Ambiguous Sensor Faults

Iglesis, EnzoONERA
Dahia, KarimONERA
Piet-Lahanier, HeleneONERA
Merlinge, NicolasONERA
Horri, NadjimUniversity of Coventry
Brusey, JamesCoventry University
Keywords: Fault detection and diagnosis, Particle filtering/Monte Carlo methods, Diagnosis of discrete event and hybrid systems
Abstract: Sensor or actuator faults occurring on a Unmanned Aerial Vehicle (UAV) can compromise the system integrity. Fault diagnosis methods is then becoming a required feature for those systems. In this paper, the focus is on fault estimation for a fixed-wing UAVs in the presence of simultaneous sensor faults. The altitude measurements of a UAV are commonly obtained from the combination of two different types of sensors: a Global Navigation Satellite System (GNSS) receiver and a barometer. Both sensors are subject to additive abrupt faults. To deal with the multimodal nature of the faulty modes, a Jump-Markov Regularized Particle Filter (JMRPF) is proposed in this paper to estimate the barometric altitude and GNSS altitude measurement faults, including the case when both faults occur simultaneously. This method is based on a regularization step that improves the robustness thanks to the approximation of the conditional density by a kernel mixture. In addition, the new jump strategy estimates the correct failure mode in 100% of the 100 simulations performed in this paper. This approach is compared with an Interacting Multiple Model Kalman Filter (IMM-KF) and the results show that the JMRPF outperforms the IMM-KF approach, particularly in the ambiguous case when both sensors are simultaneously subject to additive abrupt faults.
Paper VI111-11.22 
PDF · Video · Sensitivity Analysis of Bias in Satellite Sea Surface Temperature Measurements

Eichhorn, MikeTechnische Universität Ilmenau
Shardt, Yuri A.W.Technical University of Ilmenau
Gradone, JosephTeledyne Webb Research
Allsup, BenTeledyne Webb Research
Keywords: Fault detection and diagnosis, Particle filtering/Monte Carlo methods, Randomized methods
Abstract: The satellite sea surface temperature (SST) measurement is based on the detection of ocean radiation using microwave or infrared wavelengths within the electromagnetic spectrum. The radiance of individual wavelengths can be converted into brightness temperatures for using in SST determination. The calibration and validation of the determined SST data require reference measurements from in-situ observations. These in-situ observations are from various platforms such as ships, drifters, floats and mooring buoys and require a high measurement accuracy. This paper presents an investigation about the possibility of using a glider as possible in-situ platform. A glider is a type of autonomous underwater vehicle (AUV) which can log oceanographic data over a period of up to one year by following predetermined routes. In contrast to buoys, a glider allows a targeted investigation of regional anomalies in SST circulations. To assess the quality of SST observations from a glider, logged data from a glider mission in the Atlantic Ocean from 2018 to 2019 and corresponding satellite SST data were used. The influence of variables (e.g. measurement depth, latitude, view zenith angle, local solar time) of the bias between satellite and glider SST data was investigated using sensitivity analysis. A new and efficient distribution-based method for global sensitivity analyzes, called PAWN, was used successfully. Interested readers will find information about its operation principle and the usage for passive observations where only ``given-data'' are available.
Paper VI111-11.23 
PDF · Video · Distributed Detection and Isolation of Covert Cyber Attacks for a Class of Interconnected Systems

Al-Dabbagh, AhmadImperial College London
Barboni, AngeloImperial College London
Parisini, ThomasImperial College & Univ. of Trieste
Keywords: Fault detection and diagnosis, Secure networked control systems
Abstract: This paper deals with a topology for a class of interconnected systems, referred to as a highly interconnected system, consisting of interconnected plants and local controllers. We address the respective cyber attack surfaces as well as a design approach for detection and isolation of covert cyber attacks. For each pair of plant and controller, a cyber attack is implemented by a malicious agent, and its detection and isolation are achieved by associating the controller with two observers. These observers estimate the states of the plant, and compare the estimated states to determine if a neighbouring plant is under a covert cyber attack. The paper presents the modelling of the topology, the analysis of the covertness of cyber attacks, the design approach for the detection and isolation as well as a required existence condition. Simulation results are provided for the application of the design approach to interconnected pendula systems that are subject to a covert cyber attack.
Paper VI111-11.24 
PDF · Video · Distributed Fault Diagnosis for a Class of Time-Varying Systems Over Sensor Networks with Stochastic Protocol

Liu, YuxiaChina University of Petroleum (East China)
Sheng, LiChina University of Petroleum (East China)
Gao, MingChina University of Petroleum (East China)
Keywords: Fault detection and diagnosis, Sensor networks, Estimation and filtering
Abstract: This paper is concerned with the distributed fault diagnosis problem for a class of time-varying systems over sensor networks with nonlinearity and uncertainty. For the purpose of solving the problem of data conflict, the stochastic protocol is used to determine which node has the right to send data to the estimator at a certain transmission time. The aim of this paper is to design a set of distributed estimators to detect, isolate and estimate fault signals. The upper bound of estimation error covariance is obtained by solving two recursive matrix equations and the upper bound can be minimized by designing appropriate estimator gain at each step. Finally, a numerical example is provided to show the effectiveness of the proposed design scheme.
Paper VI111-11.25 
PDF · Video · A Preventive Maintenance Strategy for an Actuator Using Markov Chains

Alina, PricopieDunarea De Jos University of Galati
Frangu, LaurentiuDunarea De Jos University of Galati
Vilanova, RamonUniversitat Autònoma De Barcelona
Caraman, SergiuDunarea De Jos University
Keywords: Fault detection and diagnosis, Stochastic system identification, Synthesis of stochastic systems
Abstract: This paper deals with a proactive maintenance strategy used to increase the reliability of equipment. A predicting schedule of the renewal interventions will be proposed so as to ensure optimal maintenance for the equipment. Hence, the goal is to find the optimal time which is the most profitable to carry out the equipment renewal operations. The deterioration process is modeled by Markov chains, which is capable to provide information about the tendency of the equipment state. For the optimization of the maintenance a preventive strategy based on the average maintenance cost was used. The minimum maintenance average cost corresponds to the optimal time when it is most efficient to stop the equipment operation and to renew it.
Paper VI111-11.26 
PDF · Video · A Novel Fault Diagnosis Method Based on Stacked LSTM

Zhang, QingqingUniversity of Electronic Science and Technology of China
Zhang, JiyangUniversity of Electronic Science and Technology of China
Zou, JianxiaoSchool of Automation Engineering, University of Electronic Scien
Shicai, FanUniversity of Electronic Science and Technology of China
Keywords: Fault detection and diagnosis, Time series modelling, Machine learning
Abstract: Fault diagnosis is essential to ensure the operation security and economic efficiency of the chemical system. Many fault diagnosis methods have been designed for the chemical process, but most of them ignore the temporal correlation in the sequential observation signals of the chemical process. A novel deep learning method based on Stacked Long Short-Term Memory (LSTM) neural network is proposed, which can effectively model sequential data and detect the abnormal values. The proposed method is also able to fully exploit the long-term dependencies information in raw data and adaptively extract the representative features. The dataset of Tennessee Eastman (TE) process is utilized to verify the practicability and superiority of the proposed method. Extensive experimental results show that the fault detection and diagnosis model we proposed has an excellent performance when compared with several state-of-the-art baseline methods.
Paper VI111-11.27 
PDF · Video · Mixed Stochastic Process Modelling for Accelerated Degradation Testing

Li, YangNanjing University of Aeronautics and Astronautics
Liu, YueChina North Vehicle Research Institute
Zio, EnricoEcole Centrale Paris, Supelec and Politecnico Di Milano
Lu, NingyunNanjing University of Aeronautics and Astronautics
Wang, XiuliNanjing University of Aeronautics and Astronautics
Jiang, BinNanjing University of Aeronautics and Astronautics
Keywords: Fault detection and diagnosis, Time series modelling, Mechanical and aerospace estimation
Abstract: Accelerated degradation testing (ADT) is used to efficiently assess the reliability and lifetime of a high reliable products under normal stress. In general, it is common in practice to build stochastic models of degradation under a single failure mechanism based on the ADT data. However, in real applications, multi-failure mechanisms may influence the degradation process. Motivated by this, a mixed stochastic process model for ADT is proposed in this paper. The mixed stochastic process combines two singlestochastic processes with weights determined by a quantitative method that establishes the relationship with accelerated stress. After the unknown parameter estimation, the proposed model under normal stress level can be obtained. The results show that the proposed model can be used for ADT modeling under multi-failure mechanisms.
Paper VI111-11.28 
PDF · Video · Condition Monitoring of Electric-Cam Mechanisms Based on Model-Of-Signals of the Drive Current Higher-Order Differences

Barbieri, MatteoAlma Mater Studiorum - University of Bologna
Diversi, RobertoUniversity of Bologna
Tilli, AndreaUniversity of Bologna
Keywords: Fault detection and diagnosis, Time series modelling, Recursive identification
Abstract: Condition monitoring of electric motor driven mechanisms is of great importance in industrial machines. The knowledge of the actual health state of such components permits to address maintenance policies which results in better exploitation of their actual operational life span and consequently in maintenance cost reduction. In this paper, we exploit the way electric cams are implemented on the vast majority of PLC/Motion controllers to develop a suitable condition monitoring procedure. This technique relies on computing the higher-order differences of the current absorbed by slave motors to get signals that do not depend on a priori knowledge of the cam trajectory and of the mechanism nominal model. Subsequently, we will use these data in the Model-of-Signals framework, to gather information on the mechanism's health condition, which in turn can be used to perform predictive maintenance policies. The differenced signal is modelled as an ARMA process and the model capabilities in condition monitoring are then shown in simulation and experimental application. Besides, this framework allows exploiting the edge-computing capabilities of the machinery controllers by implementing recursive estimation algorithms.
Paper VI111-11.29 
PDF · Video · A Timed Model for Discrete Event System Identification and Fault Detection

de Souza, Ryan Pitanga CletoFederal University of Rio De Janeiro
Moreira, Marcos VicenteUniv. Fed. Rio De Janeiro
Lesage, Jean-JacquesENS De Cachan
Keywords: Closed loop identification, Fault detection and diagnosis, Diagnosis of discrete event and hybrid systems
Abstract: We present in this paper a timed discrete event model for system identification with the aim of fault detection, called Timed Automaton with Outputs and Conditional Transitions (TAOCT). The TAOCT is an extension of a recent untimed model proposed in the literature, called Deterministic Automaton with Outputs and Conditional Transitions (DAOCT). Differently from the DAOCT, where only the logical behavior of the discrete event system is considered, the TAOCT takes into account information about the time that the events are observed, and, for this reason, it can be used for the detection of faults that cannot be detected by using untimed models, such as faults that lead the fault detector to deadlocks. The TAOCT represents the fault-free system behavior, and a fault is detected when the observed behavior is different from the behavior predicted by the model, considering both logical and timing information. A practical example is used to illustrate the results of the paper.
Paper VI111-11.30 
PDF · Video · Design of Hypervelocity-Impact Damage Assessment Technique Based on Variational Bayesian

Zhang, HaonanUniversity of Electronic Science and Technology of China
Yin, ChunUniversity of ElectronicScience and Technology of China, Chengdu6
Huang, XuegangChina Aerodynamics Research & Development Center
Dadras, SaraUtah State University
Chen, KaiUniversity of Electronic Science and Technology of China
Dadras, SoudehUC Merced
Zhu, BingBeihang University
Keywords: Mechanical and aerospace estimation, Bayesian methods, Fault detection and diagnosis
Abstract: In this paper, a damage assessment framework based on the infrared technology is proposed to assess the damage of the spacecraft. This framework mainly contains three steps. Firstly, a damage reconstruction model based on sparse model is proposed to reconstruct the damage image of different layers. To estimate the parameter of the model, variational Bayesian is used for calculating the parameters. Secondly, a damage extraction method is used to eliminate noise in the images. At the same time, this procedure can effectively make the weak subsurface damage more clear. Finally, in order to compare the location of surface and subsurface damage, image fusion method is used to achieve damage fusion. In the experiment, the proposed framework is used for the Whipple shield detection, both images and evaluation parameters show the effectiveness and high-accuracy of the new model.
Paper VI111-11.31 
PDF · Video · GMM-Based Automatic Defect Recognition Algorithm for Pressure Vessels Defect Detection through ECPT

Yang, XiaoUniversity of Electronic Science and Technology of China
Huang, XuegangChina Aerodynamics Research & Development Center
Yin, ChunUniversity of ElectronicScience and Technology of China, Chengdu6
Cheng, Yu-huaUniversity of Electronic Science and Technology of China
Dadras, SaraUtah State University
Keywords: Mechanical and aerospace estimation, Fault detection and diagnosis, Stochastic hybrid systems
Abstract: In order to realize the automatic identification of pressure vessel defects, an improved adaptive defect recognition feature extraction algorithm through ECPT (Eddy current pulsed thermography) is proposed. The proposed feature extraction algorithm consists of five elements: thermal image data segmentation, variable interval search, probability density function modeling, data classification, and reconstructed image acquisition. The combination of data block selection and variable interval search can reduce the double counting. And the KG-EM (Kmeans-GMM-EM) algorithm is proposed to obtain the Gaussian mixture model corresponding to the classification, and thus the corresponding probability is obtained to classify the TTRs (Transient Thermal Response). The reconstructed thermal image is obtained by the classified TTRs. This method can extract the main information of the image accurately and efficiently. Experimental results are provided to demonstrate their effectiveness.
Paper VI111-11.32 
PDF · Video · Health-Aware LPV Model Predictive Control of Wind Turbines (I)

Boutros, KhouryUPC
Nejjari, FatihaUniversitat Politecnica De Catalunya
Puig, VicençUniversitat Politècnica De Catalunya (UPC)
Keywords: Supervisory control and automata, Fault detection and diagnosis
Abstract: Wind turbines components are subject to considerable stresses and fatigue due to extreme environmental conditions to which are exposed, especially those located offshore. Interest in the integration of control with life estimation of components has increased in recent years. The integration of a systems health management module with MPC control provides the wind turbine a mechanism to operate safely and optimize the tradeoff between components life and energy production. In this paper, a health-aware LPV model predictive control approach for wind turbines is proposed. The proposed controller establish a trade-off between the economic objective based on maximizing the energy production but at the same time maximizing the remaining useful life. The controller uses an LPV model for dealing with the non-linearity of the wind turbine model. The proposed approach is tested on a well-known wind turbine case study.
Paper VI111-11.33 
PDF · Video · Interpretable Deep Learning for Monitoring Combustion Instability

Gangopadhyay, TryambakIowa State University
Tan, Sin YongIowa State University
Locurto, AnthonyIowa State University
Michael, James B.Iowa State University
Sarkar, SoumikIowa State University
Keywords: Machine learning, Fault detection and diagnosis, Mechanical and aerospace estimation
Abstract: Transitions from stable to unstable states occurring in dynamical systems can be sudden leading to catastrophic failure and huge revenue loss. For detecting these transitions during operation, it is of utmost importance to develop an accurate data-driven framework that is robust enough to classify stable and unstable scenarios. In this paper, we propose deep learning frameworks that show remarkable accuracy in the classification task of combustion instability on carefully designed diverse training and test sets. We train our model with data from a laboratory-scale combustion system showing stable and unstable states. The dataset is multimodal with correlated data of hi-speed video and acoustic signals. We develop a labeling mechanism for sequences by implementing Kullback–Leibler Divergence on the time-series data. We develop deep learning frameworks using 3D Convolutional Neural Network and Long Short Term Memory network for this classification task. To go beyond the accuracy and to gain insights into the predictions, we incorporate attention mechanism across the time-steps. This aids in understanding the time-periods which contribute significantly to the prediction outcome. We validate the insights from a domain knowledge perspective. By exploring inside the accurate black-box models, this framework can be used for the development of better detection frameworks in different dynamical systems.
Paper VI111-11.34 
PDF · Video · Memoryless Cumulative Sign Detector for Stealthy CPS Sensor Attacks

Bonczek, PaulUniversity of Virginia
Bezzo, NicolaUniversity of Virginia
Keywords: Fault Detection, Diagnosis, Identification, Isolation and Tolerance for Autonomous Vehicles, Modeling, supervision, control and diagnosis of automotive systems, Autonomous Vehicles
Abstract: Stealthy false data injection attacks on cyber-physical systems introduce erroneous measurements onto sensors with the intent to degrade system performance. An intelligent attacker can design stealthy attacks with knowledge of the system model and noise characteristics to evade detection from state-of-the-art fault detectors by remaining within detection thresholds. However, during these hidden attacks, an attacker with the intention of hijacking a system will leave traces of non-random behavior that contradict with the expectation of the system model. Given these premises, in this paper we propose a run-time monitor called Cumulative Sign (CUSIGN) detector, for identifying stealthy falsified measurements by identifying if measurements are no longer behaving in a random manner. Specifically, our proposed CUSIGN monitor considers the changes in sign of the measurement residuals and their expected occurrence in order to detect if a sensor could be compromised. Moreover, our detector is designed to be a memoryless procedure, eliminating the need to store large sequences of data for attack detection. We characterize the detection capabilities of the proposed CUSIGN technique following the well-known chi2 failure detection scheme. Additionally, we show the advantage of augmenting CUSIGN to the model-based Cumulative Sum (CUSUM) detector, which provides magnitude bounds on attacks, for enhanced detection of sensor spoofing attacks. Our approach is validated with simulations on an unmanned ground vehicle (UGV) during a navigation case study.
VI111-12
Identification for Control Regular Session
Chair: Tanaka, HideyukiHiroshima University
Co-Chair: Mitrishkin, YuriM.V. Lomonosov Moscow State University
Paper VI111-12.1 
PDF · Video · Model Error Modelling Using a Stochastic Embedding Approach with Gaussian Mixture Models for FIR Systems

Orellana Prato, Rafael AngelUniversidad Técnica Federico Santa Maria
Carvajal, RodrigoUniversidad Tecnica Federico Santa Maria
Aguero, Juan C.Universidad Santa Maria
Goodwin, Graham C.University of Newcastle
Keywords: Identification for control
Abstract: In this paper a Maximum Likelihood estimation algorithm for error-model modelling using a stochastic embedding approach is developed. The error-model distribution is approximated by a finite Gaussian mixture. An Expectation-Maximization based algorithm is proposed to estimate the nominal model and the distribution of the parameters of the error-model by using the data from independent experiments. The benefits of our proposal are illustrated via numerical simulations.
Paper VI111-12.2 
PDF · Video · Identification of Ill-Conditioned Systems Using Output Rotation

Friman, MatsNeles
Keywords: Identification for control
Abstract: A new method for identification of ill-conditioned systems is suggested. Our aim is to provide a solution that is practical and functional in the sense that no initial knowledge about process is needed, light-weight tools can be used for identification (e.g. simple ARX models with standard least-squares regression), and model structures with minimal number of parameters and states are used. The main idea is to employ principal component analysis (PCA) to rotate the outputs before identifying the process in directions important for control.
Paper VI111-12.3 
PDF · Video · The Plasma Shape Control System in the Tokamak with the Artificial Neural Network As a Plasma Equilibrium Reconstruction Algorithm

Prokhorov, ArtemLomonosov Moscow State University
Mitrishkin, YuriM.V. Lomonosov Moscow State University
Korenev, PavelV.A. Trapeznikov Institute of Control Sciences
Patrov, MikhailIoffe Physical Technical Institute of the Russian Academy of Sci
Keywords: Identification for control, Closed loop identification, Experiment design
Abstract: The problem of accurate plasma shape control is significant, both for modern tokamaks, for example for the Globus-M/M2 spherical tokamak, and for future thermonuclear tokamak-reactors using magnetic plasma confinement. The article presents the new results of design and modeling the plasma shape control system for the Globus-M/M2 spherical tokamak with the pre-trained neural network as a plasma equilibrium reconstruction algorithm, which is included in the feedback of the system. To collect the necessary data for training the neural network the developed magnetic plasma evolutionary code was used.
Paper VI111-12.4 
PDF · Video · EM-Based Identification of Static Errors-In-Variables Systems Utilizing Gaussian Mixture Models

Cedeño, Angel L.Universidad Técnica Federico Santa María
Orellana Prato, Rafael AngelUniversidad Técnica Federico Santa Maria
Carvajal, RodrigoUniversidad Tecnica Federico Santa Maria
Aguero, Juan C.Universidad Santa Maria
Keywords: Errors in variables identification
Abstract: In this paper we address the problem of identifying a static errors-in-variables system. Our proposal is based on the Expectation-Maximization algorithm, in which we consider that the distribution of the noise-free input is approximated by a finite Gaussian mixture. This approach allows us to estimate the static system parameters, the input and output noise variances, and the Gaussian mixture parameters. We show the benefits of our proposal via numerical simulations.
Paper VI111-12.5 
PDF · Video · A Data-Driven Immersion Technique for Linearization of Discrete-Time Nonlinear Systems

Wang, ZhemingUniversité Catholique De Louvain
Jungers, Raphaël M.Université Catholique De Louvain
Keywords: Identification for control, Nonlinear system identification, Time series modelling
Abstract: This paper proposes a data-driven immersion approach to obtain linear equivalents or approximations of discrete-time nonlinear systems. Exact linearization can only be achieved for very particular classes of systems. In general cases, we aim to obtain a finite-time linear approximation. Our approach only takes a finite set of trajectories and hence an analytic model is not required. The mismatch between the approximate linear model and the original system is concretely discussed with formal bounds. We also provide a Koopman-operator interpretation of this technique, which shows a link between system immersibility and the Koopman operator theory. Several numerical examples are taken to show the capabilities of the proposed immersion approach. Comparison is also made with other Koopman-based lifting approaches which use radial basis functions and monomials.
Paper VI111-12.6 
PDF · Video · Identification of a Class of Hybrid Dynamical Systems

Massaroli, StefanoThe University of Tokyo
Califano, FedericoUniversity of Twente
Faragasso, AngelaThe Univeristy of Tokyo
Risiglione, MattiaETH Zurich
Yamashita, AtsushiThe University of Tokyo
Asama, HajimeThe University of Tokyo
Keywords: Recursive identification, Identification for control, Discrete event modeling and simulation
Abstract: This paper presents a novel identification procedure for a class of hybrid dynamical systems. In particular, we consider hybrid dynamical systems which are single flowed and single jumped and whose flow and jump maps linearly depend on two sets of unknown parameters. A systematic way to determine whether the system is flowing or jumping is introduced and used to identify the unknown parameters by employing a linear recursive estimator. Simulations have been performed to prove the validity of the proposed methodology. Results proved the efficiency and accuracy of the developed identification procedure.
Paper VI111-12.7 
PDF · Video · Efficient Iterative Solvers in the Least Squares Method

Stotsky, Alexander A.Chalmers University of Technology
Keywords: Recursive identification, Identification for control, Time series modelling
Abstract: Fast convergent, accurate, computationally efficient, parallelizable, and robust matrix inversion and parameter estimation algorithms are required in many time-critical and accuracy-critical applications such as system identification, signal and image processing, network and big data analysis, machine learning and in many others. This paper introduces new composite power series expansion with optionally chosen rates (which can be calculated simultaneously on parallel units with different computational capacities) for further convergence rate improvement of high order Newton-Schulz iteration. New expansion was integrated into the Richardson iteration and resulted in significant convergence rate improvement. The improvement is quantified via explicit transient models for estimation errors and by simulations. In addition, the recursive and computationally efficient version of the combination of Richardson iteration and Newton-Schulz iteration with composite expansion is developed for simultaneous calculations. Moreover, unified factorization is developed in this paper in the form of tool-kit for power series expansion, which results in a new family of computationally efficient Newton-Schulz algorithms.
Paper VI111-12.8 
PDF · Video · An Estimation Method of Innovations Model in Closed-Loop Environment with Lower Horizons

Ikeda, KenjiTokushima University
Tanaka, HideyukiHiroshima University
Keywords: Subspace methods, Closed loop identification, Identification for control
Abstract: This paper proposes an estimation method of the innovations model in closed loop environment by using the estimate of the innovations process. The estimate of the innovations process from the finite interval of data has a bias, so are the estimate of the proposed method. However, it is analyzed that the bias can be reduced. The Kalman gain and the covariance of the innovations process are estimated by using a semi-definite programming problem previously proposed by the authors. Numerical simulation illustrates the proposed method gives better performance than Closed-Loop MOESP and PBSID when the data length is large and the past horizon is selected low.
Paper VI111-12.9 
PDF · Video · MPC Closed-Loop Identification without Excitation

Zhu, YunZhejiang University
Yan, WengangZhejiang University
Zhu, YucaiZhejiang University
Keywords: Closed loop identification, Identification for control, Identifiability
Abstract: This paper presents a method of closed-loop identification for multivariable systems without external excitation. The method is specially designed for model predictive control (MPC) systems. Without using external excitation (test signals), the method ensures the informativity of the closed-loop data and, at the same time, improve the control performance during the test period. The purpose of the study is to reduce the cost of identification test. The basic idea is to switch the input weighting matrix in the MPC controller which leads to the informativity of the data-set. A preliminary test is carried out in order to find a new input weighting matrix which improve the control performance; then a switching scheme is developed based on the two weighting matrixes. Traditional simulation based model validation no longer works in closed-loop identification without excitation, and model error bounds on the frequency responses can be used instead. The effectiveness of the proposed method is demonstrated by a simulation study.
VI111-13
Linear Systems Identification Regular Session
Chair: Mevel, LaurentINRIA
Co-Chair: Ushirobira, RosaneInria
Paper VI111-13.1 
PDF · Video · Identification of Noisy Input-Output FIR Models with Colored Output Noise

Barbieri, MatteoAlma Mater Studiorum - University of Bologna
Diversi, RobertoUniversity of Bologna
Keywords: Errors in variables identification
Abstract: This paper deals with the identification of FIR models corrupted by white input noise and colored output noise. An identification algorithm that exploits the properties of both the dynamic Frisch scheme and the high-order Yule-Walker (HOYW) equations is proposed. It is shown how the HOYW equations allow defining a selection criterion for identifying the input noise variance (and then the FIR coefficients) within the Frisch locus of solutions. The proposed approach does not require any a priori knowledge about the input and output noise variances. The algorithm performance is assessed by means of Monte Carlo simulations.
Paper VI111-13.2 
PDF · Video · The Frisch Scheme for EIV System Identification: Time and Frequency Domain Formulations

Soverini, UmbertoUniversity of Bologna
Soderstrom, TorstenUppsala University
Keywords: Errors in variables identification, Frequency domain identification
Abstract: Several estimation methods have been proposed for identifying errors-in-variables systems, where both input and output measurements are corrupted by noise. One of the more interesting approaches is the Frisch scheme. The method can be applied using either time or frequency domain representations. This paper investigates the general mathematical and geometrical aspects of the Frisch scheme, illustrating the analogies and the differences between the time and frequency domain formulations.
Paper VI111-13.3 
PDF · Video · Blind Identification of Two-Channel FIR Systems: A Frequency Domain Approach

Soverini, UmbertoUniversity of Bologna
Soderstrom, TorstenUppsala University
Keywords: Errors in variables identification, Frequency domain identification, Channel estimation/equalisation
Abstract: This paper describes a new approach for the blind identification of a two-channel FIR system from a finite number of output measurements, in the presence of additive and uncorrelated white noise. The proposed approach is based on frequency domain data and, as a major novelty, it enables the estimation to be frequency selective. The features of the proposed method are analyzed by means of Monte Carlo simulations. The benefits of filtering the data and using only part of the frequency domain is highlighted by means of a numerical example.
Paper VI111-13.4 
PDF · Video · Decoupling of Discrete-Time Dynamical Systems through Input-Output Blending

Baar, TamasHungarian Academy of Sciences, Institute for Computer Science An
Bauer, PeterInstitute for Computer Science and Control
Luspay, TamásInstitiute for Computer Science and Control
Keywords: Subspace methods
Abstract: This paper presents a subsystem decoupling method for Linear Time Invariant Discrete-time systems. The aim is to control a selected subsystem, while not affecting the remaining dynamics. The paper extends earlier continuous time results to discrete time systems over a finite frequency interval. Decoupling is achieved by suitable input and output blend vectors, such that they maximize the sensitivity of the selected subsystem, while at the same time they minimize the transfer through the undesired dynamics. The proposed algorithm is based on an optimization problem involving Linear Matrix Inequalities, where the H minus index of the controlled subsystem is maximized, while the transfer through the dynamics to be decoupled is minimized by a sparsity like criteria. The present approach has the advantage that it is directly applicable to stable and unstable subsystems also. Numerical examples demonstrate the effectiveness of the method.
Paper VI111-13.5 
PDF · Video · Existence and Uniqueness of Solution for Discontinuous Conewise Linear Systems

Şahan, GökhanIzmir Institute of Technology
Keywords: Subspace methods, Stability and stabilization of hybrid systems
Abstract: In this study, we give necessary and sufficient conditions for well posedness of Conewise Linear Systems in 3-dimensional space where the vector field is allowed to be discontinuous. The conditions are stated using the subspaces derived from subsystem matrices and the results are compared with the existing conditions given in the literature. We show that even we don’t have a fixed structure on system matrices as in bimodal systems, similar subspaces and numbers again determines well posedness.
Paper VI111-13.6 
PDF · Video · Variance Computation for System Matrices and Transfer Function from Input/output Subspace System Identification

Gres, SzymonINRIA
Döhler, MichaelInria
Mevel, LaurentINRIA
Keywords: Subspace methods, Vibration and modal analysis
Abstract: The transfer function of a linear system is defined in terms of the quadruplet of matrices (A,B,C,D) that can be identified from input and output measurements. Similarly these matrices determine the state space evolution for the considered dynamical system. Estimation of the quadruplet has been well studied in the literature from both theoretical and practical points of view. Nonetheless, the uncertainty quantification of their estimation errors has been mainly discussed from a theoretical viewpoint. For several output-only and input/output subspace methods, the variance of the (A,C) matrices can be effectively obtained with recently developed first-order perturbation-based schemes. This paper addresses the estimation of the (B,D) matrices, and the remaining problem of the effective variance computation of their estimates and the resulting transfer function. The proposed schemes are validated on a simulation of a mechanical system.
Paper VI111-13.7 
PDF · Video · Laguerre-Domain Modelling and Identification of Linear Discrete-Time Delay Systems

Bro, ViktorUppsala University
Medvedev, AlexanderUppsala University
Ushirobira, RosaneInria
Keywords: Frequency domain identification, Filtering and smoothing, Channel estimation/equalisation
Abstract: A closed-form Laguerre-domain representation of discrete linear time-invariant systems with constant input time delay is derived. It is shown to be useful in a l_2 to l_2 system identification setup (with l_2 denoting square-summables signals) often arising in biomedical applications, where the experimental protocol does not allow for persistent excitation of the system dynamics. The utility of the proposed system representation is demonstrated on a problem of drug kinetics estimation from clinical data.
VI111-14
Learning for Modeling, Identification, and Control Regular Session
Chair: Matschek, JanineOtto-von-Guericke-Universität Magdeburg
Co-Chair: Borrelli, FrancescoUniversity of California
Paper VI111-14.1 
PDF · Video · Online Gradient Descent for Linear Dynamical Systems

Nonhoff, MarkoLeibniz University Hannover
Muller, Matthias A.Leibniz University Hannover
Keywords: Learning for control
Abstract: In this paper, online convex optimization is applied to the problem of controlling linear dynamical systems. An algorithm similar to online gradient descent, which can handle time-varying and unknown cost functions, is proposed. Then, performance guarantees are derived in terms of regret analysis. We show that the proposed control scheme achieves sublinear regret if the variation of the cost functions is sublinear. In addition, as a special case, the system converges to the optimal equilibrium if the cost functions are invariant after some finite time. Finally, the performance of the resulting closed loop is illustrated by numerical simulations.
Paper VI111-14.2 
PDF · Video · Data-Driven Surrogate Models for LTI Systems Via Saddle-Point Dynamics

Martin, TimUniversity of Stuttgart
Koch, AnneUniversity of Stuttgart
Allgower, FrankUniversity of Stuttgart
Keywords: Learning for control, Bounded error identification, Identification for control
Abstract: For the analysis, simulation, and controller design of large-scale systems, a surrogate model is mostly required. The surrogate model should have small complexity while it approximates precisely the system behaviour with a bound on the error. A standard approach to compute a reduced model is given by modelling the system and applying model order reduction techniques. Contrary, we propose a data-driven approach. Hence, we derive a surrogate model of the input-output behaviour of LTI systems without knowledge of a model. Moreover, a bound on the maximal error between the system and the surrogate model is obtained. We analyse the stability and convergence of the presented schemes and we apply them on a benchmark system from the model-order-reduction literature.
Paper VI111-14.3 
PDF · Video · Structured Exploration in the Finite Horizon Linear Quadratic Dual Control Problem

Iannelli, AndreaETH Zurich
Khosravi, MohammadETH Zurich
Smith, Roy S.Swiss Federal Institute of Technology (ETH)
Keywords: Learning for control, Identification for control, Experiment design
Abstract: This paper presents a novel approach to synthesize dual controllers for unknown linear time-invariant systems with the tasks of optimizing a quadratic cost while reducing the uncertainty. To this end, a synthesis problem is defined where the feedback law has to simultaneously gain knowledge of the system and robustly optimize the cost. By framing the problem in a finite horizon setting, the trade-offs arising when the tasks include both identification and control are formally captured in the optimization problem. Results show that efficient exploration strategies are achieved when the structure of the problem is exploited.
Paper VI111-14.4 
PDF · Video · Learning Non-Parametric Models with Guarantees: A Smooth Lipschitz Regression Approach

Maddalena, Emilio Tanoweécole Polytechnique Fédérale De Lausanne
Jones, Colin N.Ecole Polytechnique Federale De Lausanne (EPFL)
Keywords: Learning for control, Machine learning, Bounded error identification
Abstract: We propose a non-parametric regression methodology that enforces the regressor to be fully consistent with the sample set and the ground-truth regularity assumptions. As opposed to the Nonlinear Set Membership technique, this constraint guarantees the attainment of everywhere differentiable surrogate models, which are more suitable to optimization-based controllers that heavily rely on gradient computations. The presented approach is named Smooth Lipschitz Regression (SLR) and provides error bounds on the prediction error at unseen points in the space. A numerical example is given to show the effectiveness of this method when compared to the other alternatives in a Model Predictive Control setting.
Paper VI111-14.5 
PDF · Video · Constrained Gaussian Process Learning for Model Predictive Control

Matschek, JanineOtto-von-Guericke-Universität Magdeburg
Himmel, AndreasOtto Von Guericke University Magdeburg
Sundmacher, KaiMax Planck Institute for Dynamics of Complex Technical Systems
Findeisen, RolfOtto-von-Guericke-Universität Magdeburg
Keywords: Learning for control, Machine learning, Grey box modelling
Abstract: Many control tasks can be formulated as tracking problems of a known or unknown reference signal. Examples are motion compensation in collaborative robotics, the synchronisation of oscillations for power systems or the reference tracking of recipes in chemical process operation. Both the tracking performance and the stability of the closed-loop system depend strongly on two factors: Firstly, they depend on whether the future reference signal required for tracking is known, and secondly, whether the system can track the reference at all. This paper shows how to use machine learning, i.e. Gaussian processes, to learn a reference from (noisy) data while guaranteeing trackability of the modified desired reference predictions within the framework of model predictive control. Guarantees are provided by adjusting the hyperparameters via a constrained optimisation. Two specific scenarios, i.e. asymptotically constant and periodic references, are discussed.
Paper VI111-14.6 
PDF · Video · On the Synthesis of Control Policies from Example Datasets

Gagliardi, DavideUniversity College Dublin
Russo, GiovanniUniversity of Salerno
Keywords: Learning for control, Machine learning, Nonparametric methods
Abstract: A framework that is becoming particularly appealing to design control algorithms is that of devising the control policy from examples (or demonstrations). At their roots these control from demonstration techniques, which are gaining considerable attention under the label of Inverse Reinforcement Learning (IRL), rely on Inverse Optimal Control and Optimization. Today, IRL/control is recognized as an appealing framework to learn policies from success stories and potential applications include planning and preferences/prescriptions learning. There is then no surprise that, over the years, a number of techniques have been developed to address the problem of devising control policies from demonstrations, mainly in the context of Markov Decision Processes (MDPs). In this extended abstract we introduce an approach to synthesize control policies from examples. This approach formalizes the control problem as an optimization problem where the Kullback-Leibler Divergence between an ideal probability density function (pdf, obtained from e.g. demonstrations) and the pdf modeling the system/plant is minimized. A key technical novelty of our results lies in the fact that we explicitly embed actuation constraints in our formulation, thus solving an optimization problem where the Kullback-Leibler Divergence is minimized subject to constraints on the control variable. One of the main advantages of our results over classic Inverse Reinforcement Learning (Inverse Control) approaches is that policies can be synthesized from data without requiring that the system is a MDP. Moreover, by embedding actuation constraints into the problem formulation and by solving the resulting optimization, we can export the policy that has been learned on other systems that have different actuation capabilities. As an additional contribution, we devise from our theoretical results an algorithmic procedure. The key reference applications over which the algorithm was tested involved an autonomous driving use case and full results will be presented at the conference.
Paper VI111-14.7 
PDF · Video · Modeling of Dynamical Systems Via Successive Graph Approximations

Nair, SiddharthUniversity of California, Berkeley
Bujarbaruah, MonimoyUC Berkeley
Borrelli, FrancescoUniversity of California
Keywords: Learning for control, Nonparametric methods, Identification for control
Abstract: A non-parametric technique for modeling of systems with unknown nonlinear Lipschitz dynamics is presented. The key idea is to successively utilize measurements to approximate the graph of the state-update function of the system dynamics using envelopes described by quadratic constraints. The proposed approach is then used for computing outer approximations of the state-update function using convex optimization. We highlight the efficacy of the proposed approach via a detailed numerical example.
Paper VI111-14.8 
PDF · Video · GP3: A Sampling-Based Analysis Framework for Gaussian Processes

Lederer, ArminTechnical University of Munich
Kessler, MarkusTechnical University of Munich
Hirche, SandraTechnical University of Munich
Keywords: Machine learning, Learning for control, Bayesian methods
Abstract: Although machine learning is increasingly applied in control approaches, only few methods guarantee certifiable safety, which is necessary for real world applications. These approaches typically rely on well-understood learning algorithms, which allow formal theoretical analysis. Gaussian process regression is a prominent example among those methods, which attracts growing attention due to its strong Bayesian foundations. Even though many problems regarding the analysis of Gaussian processes have a similar structure, specific approaches are typically tailored for them individually, without strong focus on computational efficiency. Thereby, the practical applicability and performance of these approaches is limited. In order to overcome this issue, we propose a novel framework called GP3, general purpose computation on graphics processing units for Gaussian processes, which allows to solve many of the existing problems efficiently. By employing interval analysis, local Lipschitz constants are computed in order to extend properties verified on a grid to continuous state spaces. Since the computation is completely parallelizable, the computational benefits of GPU processing are exploited in combination with multi-resolution sampling in order to allow high resolution analysis.
Paper VI111-14.9 
PDF · Video · Active Learning for Linear Parameter-Varying System Identification

Chin, RobertThe University of Melbourne & University of Birmingham
Maass, Alejandro I.The University of Melbourne
Ulapane, NalikaUniversity of Melbourne
Manzie, ChrisThe University of Melbourne
Shames, ImanUniversity of Melbourne
Nesic, DraganUniv of Melbourne
Rowe, JonathanUniversity of Birmingham
Nakada, HayatoToyota Motor Corporation
Keywords: LPV system identification, Experiment design, Machine learning
Abstract: Active learning is proposed for selection of the next operating points in the design of experiments, for identifying linear parameter-varying systems. We extend existing approaches found in literature to multiple-input multiple-output systems with a multivariate scheduling parameter. Our approach is based on exploiting the probabilistic features of Gaussian process regression to quantify the overall model uncertainty across locally identified models. This results in a flexible framework which accommodates for various techniques to be applied for estimation of local linear models and their corresponding uncertainty. We perform active learning in application to the identification of a diesel engine air-path model, and demonstrate that measures of model uncertainty can be successfully reduced using the proposed framework.
Paper VI111-14.10 
PDF · Video · Sparse Gaussian Mixture Model Clustering Via Simultaneous Perturbation Stochastic Approximation

Boiarov, AndreiSaint Petersburg State University
Granichin, OlegSaint Petersburg State University
Keywords: Machine learning, Randomized methods
Abstract: In this paper the problem of a multidimensional optimization in unsupervised learning and clustering is studied under significant uncertainties in the data model and measurements of penalty functions. We propose a modified version of SPSA-based algorithm which maintains stability under conditions such as a sparse Gaussian mixture model. This data model is important because it can be effectively used to evaluate the noise model in many practical systems. The proposed algorithm is robust to external disturbances and is able to process data sequentially, ``on the fly''. In this paper provides a study of this algorithm and its mathematical justification. The behavior of the algorithm is illustrated by examples of its use for clustering in various difficult conditions.
Paper VI111-14.11 
PDF · Video · Nonparametric Identification of Linear Time-Varying Systems Using Gaussian Process Regression

Hallemans, NoëlVrije Universiteit Brussel
Lataire, JohnVrije Universiteit Brussel
Pintelon, RikVrije Universiteit Brussel
Keywords: Frequency domain identification, Machine learning, Nonparametric methods
Abstract: Linear time-varying systems are a class of systems, the dynamics of which evolve in time. This results in a time-varying transfer function where each frequency has a time-varying gain. In classical identification techniques, basis functions are employed to fit these time-varying gains. In this paper a new method based on Gaussian process regression is presented. The advantage of the proposed method is a more convenient model structure and model order selection.
Paper VI111-14.12 
PDF · Video · Confidence Regions for Predictions of Online Learning-Based Control

Capone, AlexandreTechnical University of Munich
Lederer, ArminTechnical University of Munich
Hirche, SandraTechnical University of Munich
Keywords: Machine learning, Stochastic system identification, Particle filtering/Monte Carlo methods
Abstract: Although machine learning techniques are increasingly employed in control tasks, few methods exist to predict the behavior of closed-loop learning-based systems. In this paper, we introduce a method for computing confidence regions of closed-loop system trajectories under an online learning-based control law. We employ a sampling-based approximation and exploit system properties to prove that the computed confidence regions are correct with high probability. In a numerical simulation, we show that the proposed approach accurately predicts correct confidence regions.
Paper VI111-14.13 
PDF · Video · A Study on Majority-Voting Classifiers with Guarantees on the Probability of Error

Carè, AlgoUniversity of Brescia, Italy
Campi, MarcoUniversity of Brescia
Ramponi, Federico AlessandroUniversità Degli Studi Di Brescia
Garatti, SimonePolitecnico Di Milano
Cobbenhagen, RoyEindhoven University of Technology
Keywords: Machine learning, Multi-agent systems, Randomized methods
Abstract: The Guaranteed Error Machine (GEM) is a classification algorithm that allows the user to set a-priori (i.e., before data are observed) an upper bound on the probability of error. Due to its strong statistical guarantees, GEM is of particular interest for safety critical applications in control engineering. Empirical studies have suggested that a pool of GEM classifiers can be combined in a majority voting scheme to boost the individual performances. In this paper, we investigate the possibility of keeping the probability of error under control in the absence of extra validation or test sets. In particular, we consider situations where the classifiers in the pool may have different guarantees on the probability of error, for which we propose a data-dependent weighted majority voting scheme. The preliminary results presented in this paper are very general and apply in principle to any weighted majority voting scheme involving individual classifiers that come with statistical guarantees, in the spirit of Probably Approximately Correct (PAC) learning.
VI111-15
Modeling, Identification and Control of Dynamic Networks Regular Session
Chair: Basar, TamerUniv. of Illinois at Urbana-Champaign
Co-Chair: Liu, GuopingUniversity of South Wales
Paper VI111-15.1 
PDF · Video · Identification of Complex Network Topologies through Delayed Mutual Information

Toupance, Pierre-AlainUniv. Grenoble Alpes, Grenoble INP, LCIS
Lefevre, LaurentUniv. Grenoble Alpes
Chopard, BastienCUI, University of Geneva
Keywords: Dynamic Networks, Distributed control and estimation, Stochastic system identification
Abstract: The definitions of delayed mutual information and multi-information are recalled. It is shown how the delayed mutual information may be used to reconstruct the interaction topology resulting from some unknown scale-free graph with its associated local dynamics. Delayed mutual information is also used to solve the community detection problem. A probabilistic voter model defined on a scale-free graph is used throughout the paper as an illustrative example.
Paper VI111-15.2 
PDF · Video · Design of Prediction-Based Estimator for Time-Varying Networks Subject to Communication Delays and Missing Data

Hu, JunHarbin Institute of Technology
Liu, GuopingUniversity of South Wales
Keywords: Dynamic Networks, Estimation and filtering, Control and estimation with data loss
Abstract: This paper is concerned with the robust optimal estimation problem based on the prediction compensation mechanism for dynamical networks with time-varying parameters, where communication delays and degraded measurements are considered. The missing measurements are characterized by some random variables governed by Bernoulli distribution, where each sensor having individual missing probability is reflected. During the signal transmissions through the communication networks, the network-induced communication delays commonly exist among the adjacent nodes transmissions and a prediction updating method is given to compensate the caused impacts. Accordingly, a time-varying state estimator with hybrid compensation scheme is constructed such that, for both the communication delays and missing measurements, a minimized upper bound matrix with regards to the estimation error covariance matrix is found and an explicit estimator parameter matrix is designed at each sampling step accordingly. Finally, the comparative simulations are given to validate the advantages of main results.
Paper VI111-15.3 
PDF · Video · Network Topology Impact on the Identification of Dynamic Network Models with Application to Autonomous Vehicle Platooning

Araujo Pimentel, GuilhermePontifícia Universidade Católica Do Rio Grande Do Sul
de Vasconcelos, RafaelPontifícia Universidade Católica Do Rio Grande Do Sul
Salton, Aurelio TergolinaUniversidade Federal Do Rio Grande Do Sul (UFRGS)
Bazanella, Alexandre S.Univ. Federal Do Rio Grande Do Sul
Keywords: Dynamic Networks, Identification for control, Identifiability
Abstract: The interconnection of complex devices in network structures has been a challenging topic in the system identification research domain. This study presents the model identification of autonomous vehicles in platoon formation, which can be cast as a dynamic network. The paper presents the comparison between two network structures: (i) a vehicle-based network, which considers the interconnection between the vehicles based only on the velocity measurements, and (ii) a sensor-based network that considers the available sensor, i.e. the velocity and the relative distance measurements. The comparison is based on the difference between the identified transfer functions and the true ones, and the analysis of the identified air resistance coefficient variances. In addition, the paper presents the identifiability requirements for both network topologies. Simulation results show that for the same data set the variance of the identified parameters can be almost five times smaller if the system is represented as a sensor-based network, but some conditions to guarantee the identifiability of this network structure must be fulfilled.
Paper VI111-15.4 
PDF · Video · Desynchronization in Oscillatory Networks Based on Yakubovich Oscillatority

Plotnikov, SergeiInstitute for Problems of Mechanical Engineering, Russian Academ
Fradkov, Alexander L.Russian Academy of Sciences
Keywords: Dynamic Networks, Multi-agent systems, Consensus
Abstract: The desynchronization problems in oscillatory networks is considered. A new desynchronization notion is introduced and desynchronization conditions are provided. The desynchronization notion is formulated in terms of Yakubovich oscillatority of the auxiliary synchronization error system. As an example, the network of diffusively coupled FitzHugh-Nagumo systems with undirected graph is considered. The simple inequality guaranteeing network desynchronization is derived. The simulation results confirm the validity of the obtained analytical results.
Paper VI111-15.5 
PDF · Video · Sparse Estimation of Laplacian Eigenvalues in Multiagent Networks

Hayhoe, MikhailUniversity of Pennsylvania
Barreras, Jorge FranciscoUniversity of Pennsylvania
Preciado, Victor M.University of Pennsylvania
Keywords: Identification for control, Multi-agent systems, Identifiability
Abstract: We propose a method to efficiently estimate the Laplacian eigenvalues of an arbitrary, unknown network of interacting dynamical agents. The inputs to our estimation algorithm are measurements about the evolution of a collection of agents (potentially one) during a finite time horizon; notably, we do not require knowledge of which agents are contributing to our measurements. We propose a scalable algorithm to exactly recover a subset of the Laplacian eigenvalues from these measurements. These eigenvalues correspond directly to those Laplacian modes that are observable from our measurements. We show how our technique can be applied to networks of multiagent systems with arbitrary dynamics in both continuous- and discrete-time. Finally, we illustrate our results with numerical simulations.
Paper VI111-15.6 
PDF · Video · Finite-Sample Analysis for Decentralized Cooperative Multi-Agent Reinforcement Learning from Batch Data

Zhang, KaiqingUniversity of Illinois at Urbana-Champaign (UIUC)
Yang, ZhuoranPrinceton
Liu, HanNorthwestern University
Zhang, TongThe Hong Kong University of Science and Technology
Basar, TamerUniv. of Illinois at Urbana-Champaign
Keywords: Machine learning, Consensus and Reinforcement learning control, Multi-agent systems
Abstract: In contrast to its great empirical success, theoretical understanding of multi-agent reinforcement learning (MARL) remains largely underdeveloped. As an initial attempt, we provide a finite-sample analysis for decentralized cooperative MARL with networked agents. In particular, we consider a team of cooperative agents connected by a time-varying communication network, with no central controller coordinating them. The goal for each agent is to maximize the long-term return associated with the team-average reward, by communicating only with its neighbors over the network. A batch MARL algorithm is developed for this setting, which can be implemented in a decentralized fashion. We then quantify the estimation errors of the action-value functions obtained from our algorithm, establishing their dependence on the function class, the number of samples in each iteration, and the number of iterations. This work appears to be the first finite-sample analysis for decentralized cooperative MARL from batch data.
Paper VI111-15.7 
PDF · Video · Online Observability of Boolean Control Networks

Wu, GuisenSouthwest University
Liyun, DaiSouthwest University
Zhiming, LiuSouthwest University
Chen, TaolueBirkbeck, University of London
Pang, JunUniversity of Luxembourg
Keywords: Identifiability, Nonlinear system identification, Identification for control
Abstract: Observabililty is an important topic of Boolean control networks (BCNs). In this paper, we propose a new type of observability named online observability to present the sufficient and necessary condition of determining the initial states of BCNs, when their initial states cannot be reset. And we design an algorithm to decide whether a BCN has the online observability. Moreover, we prove that a BCN is identifiable iff it satisfies the controllability and the online observability, which reveals the essence of identification problem of BCNs.
VI111-16
Nonlinear System Identification Regular Session
Chair: Okuda, HiroyukiNagoya University
Co-Chair: Enqvist, MartinLinköping University
Paper VI111-16.1 
PDF · Video · Nonlinear Grey-Box Identification with Inflow Decoupling in Gravity Sewers

Balla, Krisztian MarkAalborg University
Kallesøe, Carsten SkovmoseGrundfos
Schou, ChristianGrundfos Management A/S
Bendtsen, Jan DimonAalborg Univ
Keywords: Grey box modelling, Identification for control, Nonlinear system identification
Abstract: Knowing where wastewater is flowing in sewer networks is essential to optimize system operation. Unfortunately, flow in gravity-driven sewers is subject to transport delays and typically disturbed by significant domestic, ground, and rain inflows. In this work, we utilize a lumped-parameter hydrodynamic model with a bi-linear structure for identifying these delays, decouple disturbances and to predict the discharged flow. We use pumped inlet and discharged dry-weather flow data to estimate the model parameters. Under mild assumptions on the domestic and groundwater inflows, i.e. disturbances, we show that decoupling these inflows from the total discharge is possible. A numerical case study on an EPA Storm Water Management Model and experimental results on a real network demonstrate the proposed method.
Paper VI111-16.2 
PDF · Video · An Alternating Optimization Method for Switched Linear Systems Identification

Bianchi, FedericoPolitecnico Di Milano
Falsone, AlessandroPolitecnico Di Milano
Piroddi, LuigiPolitecnico Di Milano
Prandini, MariaPolitecnico Di Milano
Keywords: Hybrid and distributed system identification
Abstract: The identification of switched systems involves solving a mixed-integer optimization problem to determine the parameters of each mode dynamics (continuous part) and assign the data samples to the modes (discrete part), so as to minimize a cost criterion measuring the quality of the model on a set of input/output data collected from the system. Oftentimes, some a priori information on the switching mechanism is available, e.g., in the form of a minimum dwell time. This information can be encoded in a suitable constraint and included in the optimization problem, but this introduces a coupling between the discrete and continuous optimization variables that makes the problem harder to solve. In this paper, we propose an iterative approach to the identification of switched systems that alternates a minimization step with respect to the continuous parameters of the modes, and a minimization step with respect to the discrete variables defining the sample-mode mapping. Constraints originating from prior knowledge on the switching mechanism are taken into account after the (unconstrained) discrete optimization step through a post-processing phase. These three phases are repeated until a stopping criterion is met. A comparative numerical analysis of the proposed method shows its improved performance with respect to competitive approaches in the literature.
Paper VI111-16.3 
PDF · Video · Identification of Markov Jump Autoregressive Processes from Large Noisy Data Sets

Hojjatinia, SarahThe Pennsylvania State University
Lagoa, Constantino M.Pennsylvania State Univ
Keywords: Hybrid and distributed system identification, Identification for control
Abstract: This paper introduces a novel methodology for the identification of switching dynamics for switched autoregressive linear models. Switching behavior is assumed to follow a Markov model. The system's outputs are contaminated by possibly large values of measurement noise. Although the procedure provided can handle other noise distributions, for simplicity, it is assumed that the distribution is Normal with unknown variance. Given noisy input-output data, we aim at identifying switched system coefficients, parameters of the noise distribution, dynamics of switching and probability transition matrix of Markovian model. System dynamics are estimated using previous results which exploit algebraic constraints that system trajectories have to satisfy. Switching dynamics are computed with solving a maximum likelihood estimation problem. The efficiency of proposed approach is shown with several academic examples. Although the noise to output ratio can be high, the method is shown to be effective in the situations where a large number of measurements is available.
Paper VI111-16.4 
PDF · Video · Joint Identification and Control in Hybrid Linear Systems

Somarakis, ChristoforosPalo Alto Research Center
Matei, IonPalo Alto Research Center
Zhenirovskyy, MaksymPARC
de Kleer, JohanPARC
Chowdhury, SoumaUniversity at Buffalo
Rai, RahulBuffalo-SUNY
Keywords: Hybrid and distributed system identification, Identification for control, Learning for control
Abstract: We propose a theoretical framework for joint system identification and control on a class of stochastic linear systems. We investigate optimization algorithms for inferring endogenous and environmental parameters from data, part of which are used for control purposes. A number of non-trivial interplays among stability and performance, as well as computational challenges and fundamental limits in identification rate emerge. Our results are validated via simulation example on a quadcopter control problem.
Paper VI111-16.5 
PDF · Video · Model Structure Identification of Hybrid Dynamical Systems Based on Unsupervised Clustering and Variable Selection

Nguyen, Duc AnNagoya University
Nwadiuto, JudeNagoya University
Okuda, HiroyukiNagoya University
Suzuki, TatsuyaNagoya Univ
Keywords: Hybrid and distributed system identification, Nonlinear system identification, Identification for control
Abstract: This paper presents a systematic identification process for the hybrid dynamical system (HDS) estimating not only the coefficients but also the structure of the model. Generally speaking, the system identification is used for the HDS system that the model structure, the number of modes, and the explanatory variables of the model are unknown. In the proposed method, a quantitative index to evaluate the number of modes is deployed and the optimal number of modes is determined from the measurement. Model selection method is also introduced to determine the explanatory variables in each mode in a systematic manner. Two of piece-wise linear models which are well known as the HDS models are used for the targeting system to identify, and the validity of the proposed method is demonstrated. Finally, the result of system identification in comparison with the conventional system identification method for HDS is discussed.
Paper VI111-16.6 
PDF · Video · A Bias-Correction Approach for the Identification of Piecewise Affine Output-Error Models

Mejari, ManasIDSIA Dalle Molle Institute for Artificial Intelligence
Breschi, ValentinaPolitecnico Di Milano
Naik, Vihangkumar VinaykumarIMT School for Advanced Studies Lucca, Italy
Piga, DarioSUPSI-USI
Keywords: Hybrid and distributed system identification, Nonlinear system identification, Recursive identification
Abstract: The paper presents an algorithm for the identification of PieceWise Affine Output-Error (PWA-OE) models, which involves the estimation of the parameters defining affine submodels as well as a partition of the regressor space. For the estimation of affine submodel parameters, a bias-correction scheme is presented to correct the bias in the least squares estimates which is caused by the output-error noise structure. The obtained bias-corrected estimates are proven to be consistent under suitable assumptions. The bias-correction method is then combined with a recursive estimation algorithm for clustering the regressors. These clusters are used to compute a partition of the regressor space by employing linear multi-category discrimination. The effectiveness of the proposed methodology is demonstrated via a simulation case study.
Paper VI111-16.7 
PDF · Video · Data Informativity for the Identification of Particular Parallel Hammerstein Systems

Colin, KévinEcole Centrale De Lyon
Bombois, XavierEcole Centrale De Lyon
Bako, LaurentEcole Centrale De Lyon
Morelli, FedericoLaboratoire Ampère, Ecole Centrale De Lyon
Keywords: Identifiability, Nonlinear system identification
Abstract: To obtain a consistent estimate when performing an identification with Prediction Error, it is important that the excitation yields informative data with respect to the chosen model structure. While the characterization of this property seems to be a mature research area in the linear case, the same cannot be said for nonlinear systems. In this work, we study the data informativity for a particular type of Hammerstein systems for two commonly-used excitations: white Gaussian noise and multisine. The real life example of the MEMS gyroscope is considered.
Paper VI111-16.8 
PDF · Video · Asymptotic Prediction Error Variance for Feedforward Neural Networks

Malmström, MagnusLinköping University
Skog, IsaacKTH
Axehill, DanielLinköping University
Gustafsson, FredrikLinköping University
Keywords: Identification for control, Machine learning, Nonlinear system identification
Abstract: The prediction uncertainty of a neural network is considered from a classical system identification point of view. To know this uncertainty is extremely important when using a network in decision and feedback applications. The asymptotic covariance of the internal parameters in the network due to noise in the observed dependent variables (output) and model class mismatch, i.e., the true system cannot be exactly described by the model class, is first surveyed. This is then applied to the prediction step of the network to get a closed form expression for the asymptotic, in training data information, prediction variance. Another interpretation of this expression is as the non-asymptotic Cramér-Rao Lower Bound. To approximate this expression, only the gradients and residuals, already computed in the gradient descent algorithms commonly used to train neural networks, are needed. Using a toy example, it is illustrated how the uncertainty in the output of a neural network can be estimated.
Paper VI111-16.9 
PDF · Video · Data-Based Identifiability and Observability Assessment for Nonlinear Control Systems Using the Profile Likelihood Method

Schmitt, ThomasTechnische Universität Darmstadt
Ritter, BastianTechnische Universität Darmstadt
Keywords: Identifiability, Nonlinear system identification, Identification for control
Abstract: This paper introduces the profile likelihood method in order to assess simultaneously the parameter identifiability and the state observability for nonlinear dynamic state-space models with constant parameters. While a formal definition of a parameter’s identifiability has been used before, the novel idea is to investigate also the state’s observability by the identifiability of its initial value. Using the profile likelihood, both qualitative as well as quantitative statements are drawn from the analysis based on the nonlinear model and (possibly noisy) sensor data. A simplified wind turbine model is presented and used as an application example for the profile likelihood approach in order to investigate the model’s usability for state and parameter estimation. It is shown that the critical model parameters and initial states are identifiable in principle. The analysis with more complex models and realistic data reveals the limitations when assumptions are deliberately violated in order to meet reality.
Paper VI111-16.10 
PDF · Video · A Polytopic Box Particle Filter for State Estimation of Non Linear Discrete-Time Systems

Gatto, ThomasONERA
Meyer, LucUniv Paris Saclay
Piet-Lahanier, HeleneONERA
Keywords: Nonlinear system identification, Bounded error identification, Particle filtering/Monte Carlo methods
Abstract: The development of Particle Filters has made possible state estimation of dynamic systems presenting non-linear dynamics and potential multi-modalities. However, the efficiency of these approaches depends tightly of the required number of particles which may prove very high to approximate large range of uncertainty on the process or the measurements. To overcome this issue, the Box-Particle Filter (BPF) combines the versatility of the Particle Filter and the robustness of set-membership algorithms. The particles are replaced by boxes which represent in a compact way large variations of the estimates. Although this filter presents various advantages and requires a small number of boxes to estimate the state, the resulting estimates may prove pessimistic, as the uncertainty description as unions of axis-aligned intervals can be rather rough and doesn't account for potential dependencies between the resulting estimate components. In the proposed paper, a new version of the BPF is proposed. Boxes are replaced by polytopes (multidimensional polygons) in the filter algorithm, so that they can adapt to represent state components dependency. This modification tends to ameliorate the estimation precision (i.e. the size of the final set that includes the true state decreases) while keeping the number of required polyhedrons small. Several examples illustrate the benefits of such an approach.
Paper VI111-16.11 
PDF · Video · State-Space Kernelized Closed-Loop Identification of Nonlinear Systems

Shakib, Mohammad FahimEindhoven University of Technology
Tóth, RolandEindhoven University of Technology
Pogromsky, A. Yu.Eindhoven Univ of Technology
Pavlov, AlexeyNorwegian University of Science and Technology
van de Wouw, NathanEindhoven Univ of Technology
Keywords: Nonlinear system identification, Closed loop identification, Nonparametric methods
Abstract: In this paper, we propose a non-parametric state-space identification approach for open-loop and closed-loop discrete-time nonlinear systems with multiple inputs and multiple outputs. Employing a least squares support vector machine (LS-SVM) approach in a reproducing kernel Hilbert space framework, a nonlinear auto-regressive model with exogenous terms is identified to provide a non-parametric estimate of the innovation noise sequence. Subsequently, this estimate is used to obtain a compatible non-parametric estimate of the state sequence in an unknown basis using kernel canonical correlation analysis. Finally, the estimate of the state sequence is used together with the estimated innovation noise sequence to find a non-parametric state-space model, again using a LS-SVM approach. The performance of the approach is analyzed in a simulation study with a nonlinear system operating both in open loop and closed loop. The identification approach can be viewed as a nonlinear counterpart of consistent subspace identification techniques for linear time-invariant systems operating in closed loop.
Paper VI111-16.12 
PDF · Video · Consistent Parameter Estimators for Second-Order Modulus Systems with Non-Additive Disturbances

Ljungberg, FredrikLinköping University
Enqvist, MartinLinköping University
Keywords: Nonlinear system identification, Grey box modelling
Abstract: This work deals with a class of nonlinear regression models called second-order modulus models. It is shown that the possibility of obtaining consistent parameter estimators for these models depends on how process disturbances enter the system. Two scenarios where consistency can be achieved for instrumental variable estimators despite non-additive system disturbances are demonstrated, both in theory and by simulation examples.
Paper VI111-16.13 
PDF · Video · An Algebraic Approach to Efficient Identification of a Class of Wiener Systems

Ozbay, BengisuNortheastern University
Sznaier, MarioNortheastern University
Camps, Octavia I.Northeastern University
Keywords: Nonlinear system identification, Hybrid and distributed system identification
Abstract: This paper considers the problem of identifying the linear portion of a Wiener system, for the case of a known, but non-invertible output non-linearity. It is well known that this scenario, common in many practical applications, leads to problems that are generically NP-hard in the number of experiments. Thus, existing techniques scale poorly and are typically limited to relatively few points. The main result of this paper shows that this difficulty can be circumvented by considering an algebraic motivated approach. Specifically, we show that the problem is equivalent to identification of a switched linear system generated from the observed data. In turn, this problem can be solved by recasting it as the problem of finding the vanishing ideal of an arrangement of subspaces, a task that reduces to finding the null space of an embedded data matrix constructed from the observed data.
Paper VI111-16.14 
PDF · Video · Identification of Nonlinear Systems and Optimality Analysis in Sobolev Spaces

Novara, CarloPolitecnico Di Torino
Nicolì, AngeloPolitecnico Di Torino
Calafiore, GiuseppePolitecnico Di Torino
Keywords: Nonlinear system identification, Identification for control
Abstract: In this paper, we propose a novel approach for the identification from data of an unknown nonlinear function together with its derivatives. This approach can be useful, for instance, in the context of nonlinear system identification for obtaining models that are more reliable than the traditional ones, based on plain function approximation. Indeed, models identified by accounting for the derivatives can provide a better performance in several tasks, such as multi-step prediction, simulation, and control design. We also develop an optimality analysis, showing that models derived using this approach enjoy suitable optimality properties in Sobolev spaces. We finally demonstrate the effectiveness of the approach with a numerical example.
Paper VI111-16.15 
PDF · Video · Koopman Operator Methods for Global Phase Space Exploration of Equivariant Dynamical Systems

Sinha, SubhrajitPacific Northwest National Laboratory
Nandanoori, Sai PushpakPacific Northwest National Laboratory
Yeung, EnochBrigham Young University
Keywords: Nonlinear system identification, Identification for control, Continuous time system estimation
Abstract: In this paper, we develop the Koopman operator theory for dynamical systems with symmetry. In particular, we investigate how the Koopman operator and eigenfunctions behave under the action of the symmetry group of the underlying dynamical system. Further, exploring the underlying symmetry, we propose an algorithm to construct a global Koopman operator from local Koopman operators. In particular, we show, by exploiting the symmetry, data from all the invariant sets are not required for constructing the global Koopman operator; that is, local knowledge of the system is enough to infer the global dynamics.
Paper VI111-16.16 
PDF · Video · Estimating Koopman Invariant Subspaces of Excited Systems Using Artificial Neural Networks

Bonnert, MarcelTechnische Universität Darmstadt
Konigorski, UlrichTechnische Universität Darmstadt
Keywords: Nonlinear system identification, Identification for control, Machine learning
Abstract: In recent years, the Koopman operator was the topic of many extensive investigations in the nonlinear system identification community. Especially, when dealing with nonlinear systems no straight forward method is available to identify systems of this class. In modern data science a standard method is using artificial neural networks to extract models from data. This method is mainly used when there is a certain function behind the measured data, but little other information is available. This paper combines the Koopman framework and artificial neural networks to achieve a linear model for nonlinear systems. The structure of the network is similar to an autoencoder. The input part is the encoder which itself consists of two different parts. The first part propagates the measurements directly to the middle part. The second encoder introduces the state-space lifting which characterizes the Koopman framework. The middle layer of the network represents an estimation of a linear state-space system that acts on a Koopman operator invariant subspace. After this layer, the extended state-space must be decoded so that the outputs of the Koopman linear system are functions of the true states. The method is evaluated with a single pendulum and a nonlinear yeast glycolysis model. Additionally, we show the advantage of considering inputs as true inputs rather than additional states.
Paper VI111-16.17 
PDF · Video · Local Linear Model Tree with Optimized Structure

Hu, XiaoyanLoughborough University
Gong, YuLoughborough University
Zhao, DezongLoughborough University
Gu, WenLoughborough University
Keywords: Nonlinear system identification, Machine learning, Grey box modelling
Abstract: This paper investigates the local linear model tree (LOLIMOT) with optimized structure. The performance of the LOLIMOT model depends on how the neurons are constructed. In the typical LOLIMOT model, the number of neurons is initially set as one and starts to increase by repeatedly splitting an existing neuron into two equal ones until the required performance is achieved. Because the equal split of a neuron is not optimal, a large model size is often necessary for required performance, leading to high complexity and strong overfitting. In this paper, we propose a gradient-decent-search-based algorithm to optimally split an existing neuron into two new ones. Based on both numerical data and simulated engine data, through the evaluation of optimized structure, the effectiveness of proposed method has been verified.
Paper VI111-16.18 
PDF · Video · Learning Koopman Operator under Dissipativity Constraints

Hara, KeitaKeio University
Inoue, MasakiKeio University
Sebe, NoboruKyushu Inst. of Tech
Keywords: Nonlinear system identification, Machine learning, Identification for control
Abstract: This paper addresses a learning problem for nonlinear dynamical systems with incorporating any specified dissipativity property. The nonlinear systems are described by the Koopman operator, which is a linear operator defined on the infinite-dimensional lifted state space. The problem of learning the Koopman operator under specified quadratic dissipativity constraints is formulated and addressed. The learning problem is in a class of the non-convex optimization problem due to nonlinear constraints and is numerically intractable. By applying the change of variable technique and the convex overbounding approximation, the problem is reduced to sequential convex optimization and is solved in a numerically efficient manner. Finally, a numerical simulation is given, where high modeling accuracy achieved by the proposed approach including the specified dissipativity is demonstrated.
Paper VI111-16.19 
PDF · Video · Deep Learning and System Identfication

Ljung, LennartLinköping University
Andersson, CarlUppsala University
Tiels, KoenEindhoven University of Technology
Schön, Thomas BoUppsala University
Keywords: Nonlinear system identification, Machine learning, Software for system identification
Abstract: Deep learning is a topic of considerable interest today. Since it deals with estimating -- or learning -- models, there are connections to the area of System Identification developed in the Automatic Control community. Such connections are explored and exploited in this contribution. It is stressed that common deep nets such as feedforward and cascadeforward nets are nonlinear ARX (NARX) models, and can thus be easily incorporated in System Identification code and practice. The case of LSTM nets is an example of NonLinear State-Space (NLSS) models. It performs worse than the cascadeforwardnet for a standard benchmark example.
Paper VI111-16.20 
PDF · Video · Extending Regularized Least Squares Support Vector Machines for Order Selection of Dynamical Takagi-Sugeno Models

Kahl, MatthiasUniversity of Kassel
Kroll, AndreasUniversity of Kassel
Keywords: Nonlinear system identification, Nonparametric methods, LPV system identification
Abstract: In this paper, the problem of order selection for nonlinear dynamical Takagi-Sugeno (TS) fuzzy models is adressed. It is solved by reformulating the TS model in its Linear Parameter Varying (LPV) form and applying an extension of a recently proposed Regularized Least Squares Support Vector Machine (R-LSSVM) technique for LPV models. For that, a nonparametric formulation of the TS identification problem is proposed which uses data-dependent basis functions. By doing so, the partition of unity of the TS model is preserved and the scheduling dependencies of the model are obtained in a nonparametric manner. For the local order selection, a regularization approach is used which forces the coefficient functions of insignificant values of the lagged input and output towards zero.
Paper VI111-16.21 
PDF · Video · Valve Stiction Model Estimation in Closed-Loop Operation

Xiong, ManZhejiang University
Zhu, YucaiZhejiang University
Keywords: Nonlinear system identification, Recursive identification, Closed loop identification
Abstract: The estimation of valve stiction model is studied. In industrial applications, valve outputs are often not available, the stiction nonlinear block appears before a linear dynamic block which is operation in a closed-loop system. By parameterizing the valve stiction model as a form of cubic splines, an identification method is proposed using a relaxation iteration scheme. Parameter estimation for the linear part is accomplished through a two-stage procedure. Firstly, an unbiased estimation is obtained by the high-order ARX (AutoRegressive eXogenous) model. Then the ARX model is reduced to a Box-Jenkins model. The consistency of the method is established. Simulation data sets and real operation data sets are used to illustrate the method.
Paper VI111-16.22 
PDF · Video · Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression

Xiao, WenxinTechnical University of Munich
Lederer, ArminTechnical University of Munich
Hirche, SandraTechnical University of Munich
Keywords: Nonparametric methods, Machine learning, Nonlinear system identification
Abstract: Modelling real world systems involving humans such as biological processes for disease treatment or human behavior for robotic rehabilitation is a challenging problem because labeled training data is sparse and expensive, while high prediction accuracy is required from models of these dynamical systems. Due to the high nonlinearity of problems in this area, data-driven approaches gain increasing attention for identifying nonparametric models. In order to increase the prediction performance of these models, abstract prior knowledge such as stability should be included in the learning approach. One of the key challenges is to ensure sufficient exibility of the models, which is typically limited by the usage of parametric Lyapunov functions to guarantee stability. Therefore, we derive an approach to learn a nonparametric Lyapunov function based on Gaussian process regression from data. Furthermore, we learn a nonparametric Gaussian process state space model from the data and show that it is capable of reproducing observed data exactly. We prove that stabilization of the nominal model based on the nonparametric control Lyapunov function does not modify the behavior of the nominal model at training samples. The flexibility and efficiency of our approach is demonstrated on the benchmark problem of learning handwriting motions from a real world dataset, where our approach achieves almost exact reproduction of the training data.
Paper VI111-16.23 
PDF · Video · On Considering the Output in Space-Filling Test Signal Designs for the Identification of Dynamic Takagi-Sugeno Models

Gringard, MatthiasUniversity of Kassel
Kroll, AndreasUniversity of Kassel
Keywords: Input and excitation design, Experiment design, Nonlinear system identification
Abstract: The model-based design of test signals for the identification of dynamical Takagi-Sugeno (TS) fuzzy models is addressed. The multi-model structure is exploited to reduce computational cost. Space-filling designs usually only address the input but the nonlinear behavior of dynamic systems depends on the lagged output in general. This is considered as an additional constraint regarding the test signal design. This contribution investigates whether a control input can be calculated exploiting the local structure and whether considering the output in space-filling designs yields identified models of higher quality.
Paper VI111-16.24 
PDF · Video · Parameter Estimation of Nonlinearly Parameterized Regressions: Application to System Identification and Adaptive Control

Ortega, RomeoSupelec
Gromov, VladislavITMO University
Nuño, EmmanuelUniversity of Guadalajara
Pyrkin, AntonITMO University
Romero Velazquez, José GuadalupeITAM
Keywords: Nonparametric methods, Nonlinear adaptive control, Identification for control
Abstract: We propose a solution to the problem of parameter estimation of nonlinearly parameterized regressions - continuous or discrete time - and apply it for system identification and adaptive control. We restrict our attention to parameterizations that can be factorized as the product of two functions, a measurable one and a nonlinear function of the parameters to be estimated. Another feature of the proposed estimator is that parameter convergence is ensured without a persistency of excitation assumption. It is assumed that, after a coordinate change, some of the elements of the transformed function satisfy a monotonicity condition. The proposed estimators are applied to design identifiers and adaptive controllers for nonlinearly parameterized systems, which are traditionally tackled using overparameterization and assuming persistency of excitation.
Paper VI111-16.25 
PDF · Video · A New Form of Rate Independence for Hysteresis Systems

Ikhouane, FaycalUniversitat Politecnica De Catalunya
Keywords: Grey box modelling
Abstract: In mathematical texts hysteresis is defined as a rate independent phenomenon, and many mathematical models of hysteresis exhibit this rate independence property. However, experiments suggest that this property is only an approximation of the hysteresis behaviour when the excitation is slow enough. Using a generalized form of the hysteretic Duhem model, we show that, although this model is not rate independent, it still satisfies a new form of rate independence. We also explore the relationship between this new form and the usual property of rate independence.
Paper VI111-16.26 
PDF · Video · Parameter Varying Mode Decoupling for LPV Systems

Baar, TamasHungarian Academy of Sciences, Institute for Computer Science An
Luspay, TamásInstitiute for Computer Science and Control
Bauer, PeterInstitute for Computer Science and Control
Keywords: LPV system identification, Subspace methods
Abstract: The paper presents the design of parameter varying input and output transformations for Linear Parameter Varying systems, which make possible the control of a selected subsystem. In order to achieve the desired decoupling the inputs and outputs of the plant are blended together, and so the MIMO control problem is reduced to a SISO one. The new input of the blended system will only interact with the selected subsystem, while the response of the undesired dynamical part is suppressed in the single output. Decoupling is achieved over the whole parameter range, and no further dynamics are introduced. Linear Matrix Inequality methods form the basis of the proposed approach, where the minimum sensitivity is maximized for the subsystem to be controlled, while the H infinity norm of the subsystem to be decoupled is minimized. The method is evaluated on a flexible wing aircraft model.
Paper VI111-16.27 
PDF · Video · Parameters Adaptive Identification of Bouc-Wen Hysteresis

Karabutov, NikolayMIREA - Russian Technological University
Shmyrin, AnatolyLipetsk State Technical University
Keywords: Modeling of manufacturing operations
Abstract: The method of structural identification of nonlinear dynamic systems is designed. It is based on the analysis of the dynamic structures offered in work. The method of construction structures is described. Structures are defined on the special informational set. The concept structurally identifiability of nonlinear dynamic system is introduced. The algorithm of structural identification is offered. The algorithm of structural identification in the conditions of uncertainty is offered.
Paper VI111-16.28 
PDF · Video · KBERG: A MatLab Toolbox for Nonlinear Kernel-Based Regularization and System Identification

Mazzoleni, MirkoUniversity of Bergamo
Scandella, MatteoUniversity of Bergamo
Previdi, FabioUniversita' Degli Studi Di Bergamo
Keywords: Software for system identification
Abstract: We present KBERG, a MatLab package for nonlinear Kernel-BasEd ReGularization and system identification. The toolbox provides a complete environment for running experiments on simulated and experimental data from both static and dynamical systems. The whole identification procedure is supported: (i) data generation, (ii) excitation signals design; (iii) kernel-based estimation and (iv) evaluation of the results. One of the main differences of the proposed package with respect to existing frameworks lies in the possibility to separately define experiments, algorithms and test, then combining them as desired by the user. Once these three quantities are defined, the user can simply run all the computations with only a command, waiting for results to be analyzed. As additional noticeable feature, the toolbox fully supports the manifold regularization rationale, in addition to the standard Tikhonov one, and the possibility to compute different (but equivalent) types of solutions other than the standard one.
Paper VI111-16.29 
PDF · Video · Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization

Yin, MingzhouETH Zurich
Iannelli, AndreaETH Zurich
Khosravi, MohammadETH Zurich
Parsi, AnilkumarETH Zurich
Smith, Roy S.Swiss Federal Institute of Technology (ETH)
Keywords: Nonparametric methods, LPV system identification
Abstract: This paper proposes a new methodology in linear time-periodic (LTP) system identification. In contrast to previous methods that totally separate dynamics at different tag times for identification, the method focuses on imposing appropriate structural constraints on the linear time-invariant (LTI) reformulation of LTP systems. This method adopts a periodically-switched truncated infinite impulse response model for LTP systems, where the structural constraints are interpreted as the requirement to place the poles of the non-truncated models at the same locations for all sub-models. This constraint is imposed by combining the atomic norm regularization framework for LTI systems with the group lasso technique in regression. As a result, the estimated system is both uniform and low-order, which is hard to achieve with other existing estimators. Monte Carlo simulation shows that the grouped atomic norm method does not only show better results compared to other regularized methods, but also outperforms the subspace identification method under high noise levels in terms of model fitting.
Paper VI111-16.30 
PDF · Video · On the Vanishing and Exploding Gradient Problem in Gated Recurrent Units

Rehmer, AlexanderUniversity of Kassel
Kroll, AndreasUniversity of Kassel
Keywords: Machine learning, Nonlinear system identification
Abstract: Recurrent Neural Networks are applied in areas such as speech recognition, natural language and video processing, and the identification of nonlinear state space models. Conventional Recurrent Neural Networks, e.g. the Elman Network, are hard to train. A more recently developed class of recurrent neural networks, so-called gated recurrent units, outperform their counterparts on virtually every task. Previous explanation attempts have not been able to explain this phenomenon in its entirety. This paper aims to provide additional insights into the differences between RNNs and gated recurrent units in order to explain the superior perfomance of gated recurrent units.
Paper VI111-16.31 
PDF · Video · A Fast Quasi-Newton-Type Method for Large-Scale Stochastic Optimisation

Wills, AdrianUniversity of Newcastle
Schön, Thomas BoUppsala University
Jidling, CarlUppsala University
Keywords: Machine learning, Nonlinear system identification, Stochastic system identification
Abstract: In recent years there has been an increased interest in stochastic adaptations of limited memory quasi-Newton methods, which compared to pure gradient-based routines can improve the convergence by incorporating second-order information. In this work we propose a direct least-squares approach conceptually similar to the limited memory quasi-Newton methods, but that computes the search direction in a slightly different way. This is achieved in a fast and numerically robust manner by maintaining a Cholesky factor of low dimension. The performance is demonstrated on real-world benchmark problems which shows improved results in comparison with already established methods.
Paper VI111-16.32 
PDF · Video · An Empirical Assessment of the Universality of ANNs to Predict Oscillatory Time Series

Dercole, FabioPolitecnico Di Milano
Sangiorgio, MatteoPolitecnico Di Milano
Schmirander, YunusPolitecnico Di Milano
Keywords: Machine learning, Time series modelling, Nonlinear system identification
Abstract: Artificial neural networks (ANNs) are universal function approximators, therefore suitable to be trained as predictors of oscillatory time series. Though several ANN architectures have been tested to predict both synthetic and real-world time series, the universality of their predictive power remained unexplored. Here we empirically test this universality across five well-known chaotic oscillators, limiting the analysis to the simplest architecture, namely multi-layer feed-forward ANN trained to predict one sampling step ahead. To compare different predictors, data are sampled according to their frequency content and the ANN structure scales with the characteristic dimensions of the oscillator. Moreover, the quality of recursive multi-step-ahead predictions are compared in terms of the system's (largest) Lyapunov exponent (LLE), i.e., the predictive power is measured in terms of the number of Lyapunov times (LT, the LLE inverse) predicted within a prescribed (relative) error. The results confirm the rather uniform predictive power of the proposed ANN architecture.
VI111-17
Particle Filtering/Monte Carlo Methods Regular Session
Chair: Hanebeck, UweKarlsruhe Institute of Technology (KIT)
Co-Chair: Imsland, LarsNorwegian University of Science and Technology
Paper VI111-17.1 
PDF · Video · Bayesian Fill Volume Estimation Based on Point Level Sensor Signals

Zumsande, JohannesLeibniz University Hannover
Kortmann, Karl-PhilippLeibniz University Hannover
Wielitzka, MarkLeibniz University Hanover
Ortmaier, TobiasGottfried Wilhelm Leibniz Universität Hannover
Keywords: Particle filtering/Monte Carlo methods, Bayesian methods, Stochastic system identification
Abstract: In dry bulk and fluid processing, the composites are usually stored in hoppers, tanks, or other containers. Due to the economic advantages, binary point level sensors, which detect fill level exceeding, are widely used for process monitoring and control. In this paper, we propose different filters for estimating the probability distribution of the fill volume based on a time-variant measurement distribution and a stochastic physical model with white process noise.

A filter based on the model prediction with separated measurement update and two Bayesian particle filters are proposed and compared with a simulated ground truth. The performance measures are the root-mean-square error, the precision of the 95% and 75% credible intervals, and the average value of the estimated probability density function at the simulated fill volumes.

Paper VI111-17.2 
PDF · Video · Auxiliary-Filter-Free Incompressible Particle Flow Filtering Using Direct Estimation of the Log-Density Gradient with Target Tracking Examples

Choe, YeongkwonSeoul National University
Park, Chan GookSeoul National Univ
Keywords: Particle filtering/Monte Carlo methods, Estimation and filtering, Mechanical and aerospace estimation
Abstract: This paper presents an incompressible particle flow filtering method that does not require an auxiliary filter by estimating log-density gradients directly from particles. Particle flow filter (PFF) is likely to avoid particle impoverishment and degeneracy problems that occur in particle filters because particles themselves move toward desired density to perform measurement updates. There are various implementation forms for PFF depending on the assumptions made about the flow. This paper deals with PFF using incompressible flow. Incompressible PFF requires the log-density gradient to calculate the flow. The well-known gradient estimation method for incompressible PFF is a finite difference method collaborating with k nearest neighbors(kNN) method. Since this method requires the prior knowledge about the prior density value in each particle, it is necessary to use an auxiliary filter or a density estimation technique. As a result, the performance of an auxiliary filter or a density estimation technique can directly affect the PFF performance, and the finite difference method is more likely to be inaccurate than directly estimating the log-density gradient. Therefore, this paper presents a PFF structure applying least-squares log-density gradient (LSLDG) method that estimates the log-density gradient directly from particles. In order to verify the performance of the presented structure, this paper performs both single and multiple target tracking simulations. Simulation results demonstrate that the presented structure has a relatively good estimation performance and works more robustly for various situations.
Paper VI111-17.3 
PDF · Video · Sampling Variance Update Method in Monte Carlo Model Predictive Control

Nakatani, ShintaroUniversity of Tsukuba
Date, HisashiUniversity of Tsukuba
Keywords: Particle filtering/Monte Carlo methods, Randomized methods
Abstract: This study describes the influence of user parameters on control performance in a Monte-Carlo model predictive control (MCMPC). MCMPC based on Monte-Carlo sampling depends significantly on the characteristics of sampling distribution. We quantified the effect of user determinable parameters on control performance using the relationship between the algorithm of MCMPC and convergence to the optimal solution. In particular, we investigated the limitations associated with the variance of sampling distribution causing a trade-off relationship with the convergence speed and accuracy of estimation. To overcome this limitation, we proposed two variance updating methods and new MCMPC algorithm. Furthermore, the effectiveness of the numerical simulation was verified.
Paper VI111-17.4 
PDF · Video · Optimal Reduction of Dirac Mixture Densities on the 2-Sphere

Frisch, DanielKIT
Li, KailaiKarlsruhe Institute of Technology (KIT)
Hanebeck, UweKarlsruhe Institute of Technology (KIT)
Keywords: Particle filtering/Monte Carlo methods, Stochastic system identification, Estimation and filtering
Abstract: This paper is concerned with optimal approximation of a given Dirac mixture density on the S2 manifold, i.e., a set of weighted samples located on the unit sphere, by an equally weighted Dirac mixture with a reduced number of components. The sample locations of the approximating density are calculated by minimizing a smooth global distance measure, a generalization of the well-known Cramér-von Mises Distance. First, the Localized Cumulative Density (LCD) together with the von Mises-Fisher kernel provides a continuous characterization of Dirac mixtures on the S2 manifold. Second, the L2 norm of the difference of two LCDs is a unique and symmetric distance between the corresponding Dirac mixtures. Thereby we integrate over all possible kernel sizes instead of choosing one specific kernel size. The resulting approximation method facilitates various efficient nonlinear sample-based state estimation methods.
Paper VI111-17.5 
PDF · Video · Probability Density Function Control for Stochastic Nonlinear Systems Using Monte Carlo Simulation

Zhang, QichunUniversity of Bradford
Wang, HongPcific Northwest National Laboratory
Keywords: Particle filtering/Monte Carlo methods, Synthesis of stochastic systems
Abstract: This paper presents an implementable framework of output probability density function (PDF) control for a class of stochastic nonlinear systems which are subjected to non-Gaussian noises. The statistical properties of the system outputs can be adjusted by shaping the dynamic output probability density function to track the reference stochastic distribution. However, the dynamic probability density function evolution is very difficult to obtain analytically even if the system model and the stochastic distributions of the noises are known. Motivated by Monte Carlo simulation, the dynamic probability density function can be estimated by sampling data which forms the contribution of this paper. In particular, the sampling points are generated following the stochastic distribution of the noise for each instant. These points go through the system would generate the histogram for system outputs, then the dynamic model can be established based on the dynamic histogram which reflects the randomness and the nonlinear dynamics of the investigated system. Based on the established model, the output probability density function tracking can be achieved and the simulation results and discussions show the effectiveness and benefits of the presented framework.
VI112
Systems and Signals - Adaptive and Learning Systems
VI112-01 Active Disturbance Rejection Control: Data-Driven Mechanisms, Analysis and Engineering Practice   Open Invited Session, 12 papers
VI112-02 History of Adaptive Control   Open Invited Session, 5 papers
VI112-03 Iterative Learning Control and Repetitive Control   Open Invited Session, 21 papers
VI112-04 Adaptive Observers and Control   Regular Session, 5 papers
VI112-05 Consensus and Reinforcement Learning Control   Regular Session, 9 papers
VI112-06 Extremum Seeking and Model-Free Adaptive Control   Regular Session, 7 papers
VI112-07 Nonlinear Adaptive Control   Regular Session, 11 papers
VI112-01
Active Disturbance Rejection Control: Data-Driven Mechanisms, Analysis and
Engineering Practice
Open Invited Session
Chair: Tan, YingThe Univ of Melbourne
Co-Chair: Xue, WenchaoChinese Academy of Sciences, Beijing 100190,
Organizer: Xue, WenchaoChinese Academy of Sciences, Beijing 100190,
Organizer: Gao, ZhiqiangCleveland State Univ
Organizer: Tan, YingThe Univ of Melbourne
Organizer: Hou, ZhongshengBeijing Jiaotong Univ
Organizer: Sira-Ramirez, HeberttCINVESTAV-IPN
Paper VI112-01.1 
PDF · Video · The Combination of Q-Learning Based Tuning Method and Active Disturbance Rejection Control for SISO Systems with Several Practical Factors (I)

Chen, SenChinese Academy of Sciences
Bai, WenyanChinese Academy of Sciences
Chen, ZhixiangRocket Force University of Engineering
Zhao, ZhiliangShaanxi Normal University
Keywords: Learning for control, Nonlinear adaptive control, Adaptive observer design
Abstract: The paper studies the control design and parameter tuning for SISO systems with several practical factors, including nonlinear uncertainty, time delay, input saturation and measurement noise. An active disturbance rejection control design is proposed to actively compensate for the above nonlinear factors. Moreover, an automatic tuning method based on Q-learning is proposed, which is featured with model-free and data-driven properties. By the tentative actions in the proposed Q-learning algorithm, the optimized control parameters can be obtained.
Paper VI112-01.2 
PDF · Video · Servo Velocity Control Using a P+ADOB Controller (I)

Luna Pineda, Jose LuisCINVESTAV
Asiain De la Luz, ErickCentro De Investigación Y De Estudios Avanzados Del IPN
Garrido-Moctezuma, Rubén AlejandroCentro De Investigacion Y De Estudios Avanzados Del I.P.N
Keywords: Adaptive observer design, Experiment design
Abstract: This paper describes preliminary results on a Proportional plus Adaptive Disturbance Observer (P+ADOB) controller applied to velocity regulation tasks in a servo system. Adaptation law is obtained to estimate the servo system input gain, which is subsequently employed in the design of a Disturbance Observer. Compared with previous approaches, this feature relaxes the assumption on exact knowledge on the input gain, and only upper and lower bounds on this term are assumed known. A stability proof assuming constant disturbances allows concluding that the estimate of the input gain is bounded, and the velocity tracking error converges to zero. Real-time experiments illustrate the performance of the proposed controller.
Paper VI112-01.3 
PDF · Video · Error Analysis of ADRC Linear Extended State Observer for the System with Measurement Noise (I)

Song, JiaBeihang University, Beijing
Zhao, MingfeiBeihang University
Gao, KeSchool of Astronautics, Beihang University, Beijing
Su, JiangchengBeihang University
Keywords: Adaptive observer design, Filtering and smoothing, Frequency domain identification
Abstract: The Active Disturbance Rejection Control (ADRC) method, which is not dependent upon the accurate system model and has strong robustness for adjusting to disturbances, is widely used in many fields. As the core of the ADRC method, the performance of the Extended State Observer (ESO) is of great importance to the controller. In practical applications, the observer will inevitably receive the influence of measurement noise, but the research on the extent of impact is less. This article takes into account observing errors caused by measurement noise, deriving and analyzing their impact on Linear Extended State Observer (LESO) performance firstly. According to the theoretical derivation and simulation analysis, an improved controller is designed, which can effectively suppress the effect of noise on the actuator and system output.
Paper VI112-01.4 
PDF · Video · Friction Compensation and Limit Cycle Suppression at Low Velocities Based on Extended State Observer (I)

Piao, MinnanNankai University
Wang, YingScience and Technology on Space Physics Laboratory
Sun, MingweiNankai University
Chen, ZengqiangNankai University
Keywords: Adaptive observer design, Mechanical and aerospace estimation
Abstract: This paper investigates the extended state observer (ESO) based friction compensation at low velocities with only the position measurement. ESO is an effective model-free friction compensation technique and thus is employed in this paper. Based on the describing function analysis, it is revealed that the higher the observer bandwidth is, the larger the velocity feedback gain should be to suppress the limit cycle. However, the available damping provided by the derivative control is restricted when the signal-to-noise ratio of the velocity is low. Under such a condition, the observer bandwidth cannot be high and the friction compensation performance is thus limited. To solve this conflict, a switching control law based on the ESO is proposed to compensate the friction in a fast manner and suppress the limit cycle simultaneously. The switching strategy aims to determine when the disturbance compensation should be added in the control signal to eliminate the friction induced oscillations without making the system response become sluggish. Such an idea is enlightened by the fact that nonlinear modifications to the integral action are always needed in practice. Hardware experiments are performed on a brushless DC motor to validate the effectiveness of the proposed compensation scheme.
Paper VI112-01.5 
PDF · Video · Half-Gain Tuning for Active Disturbance Rejection Control (I)

Herbst, GernotSiemens AG
Hempel, Arne-JensTechnische Universität Chemnitz
Göhrt, ThomasTechnische Universität Chemnitz
Streif, StefanTechnische Universität Chemnitz
Keywords: Adaptive observer design, Estimation and filtering
Abstract: A new tuning rule is introduced for linear active disturbance rejection control (ADRC), which results in similar closed-loop dynamics as the commonly employed bandwidth parameterization design, but with lower feedback gains. In this manner the noise sensitivity of the controller is reduced, paving the way for using ADRC in more noise-affected applications. It is proved that the proposed tuning gains, while rooted in the analytical solution of an algebraic Riccati equation, can always be obtained from a bandwidth parameterization design by simply halving the gains. This establishes a link between optimal control and pole placement design.
Paper VI112-01.6 
PDF · Video · New Tuning Methods of Both PID and ADRC for MIMO Coupled Nonlinear Uncertain Systems (I)

Zhong, ShengChinese Academy of Sciences
Huang, YiInstitute of Systems Science, Chinese Academy of Sciences
Guo, LeiChinese Academy of Sciences
Keywords: Nonlinear adaptive control, Adaptive observer design
Abstract: This paper proposes a new and simple design method for both the famous proportional-integral-derivative (PID) control and the active disturbance rejection control (ADRC) of multi-input multi-output (MIMO) coupled nonlinear uncertain systems. Firstly, a quantitative lower bound to the bandwidth of the parallel extended state observers (ESOs) of ADRC is given, which is not necessarily of high gain. Then, inspired by an inherent but less noticed relationship between PID and ADRC, a new and concrete PID tuning rule is introduced, which can achieve both the strong robust decoupling control and good tracking performance of the MIMO closed-loop systems. Finally, the theoretical results, which reveal that why and how both PID control and ADRC can effectively deal with decoupling problem for MIMO coupled nonlinear uncertain systems, are verified by simulations.
Paper VI112-01.7 
PDF · Video · Model-Based Feed-Forward Control for Time-Varying Systems with an Example for SRF Cavities (I)

Pfeiffer, SvenDESY Hamburg
Eichler, AnnikaDESY
Schlarb, HolgerDESY
Keywords: Time series modelling, Model reference adaptive control
Abstract: To derive feed-forward signals the impulse response matrix has to be inverted. While for time-invariant systems this matrix has a Toeplitz structure, this is not the case for time-variant systems. Thus, the derivation of the inverse scales cubically with the length of the signal horizon. This paper presents an efficient way to calculate the inverse impulse response matrix based on the description as linear fractional transformation. With this the calculation effort scales only linearly with the horizon. The feed-forward signal generation is applied in this paper for superconducting accelerating structures. The superconducting accelerating cavities are operated in pulsed mode. Each cavity is fed by a 1.3,GHz radio frequency signal with high power. Model-based feed-forward control is essential here to relief the feedback controller and with this to minimize the power consumption and therefore heating of different components. To derive a model-based feed-forward signal, first, a reasonable reference signal is to be chosen, which is done here based on physical properties of the cavities, then the efficient inversion of the impulse response matrix is applied. Experimentally results from the European X-ray free-electron laser are presented.
Paper VI112-01.8 
PDF · Video · Flatness Based ADRC Control of Lagrangian Systems: A Moving Crane (I)

Sira-Ramirez, Hebertt J.CINVESTAV-IPN
Gao, ZhiqiangCleveland State Univ
Keywords: Nonlinear adaptive control
Abstract: A procedure is described for direct tangent linearization, around a given equilibrium point, of non-linear multivariable Lagrangian systems, in terms of second order variational expansions of the Lagrangian function. When the linearized model is controllable (i.e., it exhibits the flatness property), we present an Active Disturbance Rejection Control (ADRC) scheme, valid for stabilization and flat output reference trajectory tracking tasks designed on the basis of the incremental system. The linear approach requires only generalized incremental position measurements, with no explicit need for incremental velocity observers. The ADRC controller is cast in terms of equivalent classical linear compensation networks. A moving crane example is presented which illustrates, through digital computer simulations, the effectiveness of the proposed control scheme.
Paper VI112-01.9 
PDF · Video · Disturbance Observer Based Control Design Via Active Disturbance Rejection Control: A PMSM Example (I)

Aguilar-Orduña, Mario AndrésCINVESTAV
Zurita-Bustamante, Eric WilliamCINVESTAV
Sira-Ramirez, Hebertt J.CINVESTAV-IPN
Gao, ZhiqiangCleveland State Univ
Keywords: Nonlinear adaptive control
Abstract: A new Disturbance Observer Based (DOB) controller design procedure is here obtained via a reinterpretation of the disturbance estimation scheme, present in the Extended State Observer (ESO) based Active Disturbance Rejection (ADR) control scheme. If the reinterpreted disturbance estimation process is explicitly used, now, in combination with an ADR controller, the overall total disturbance effects are substantially diminished in the feedback loop, beyond that achievable by ESO-based ADR control alone. The context is that of nonlinear differentially flat systems, simplified to Kronecker chains of integrations. A Permanent Magnet Synchronous Motor (PMSM) example is examined and its performance is assessed from an experimental setting.
Paper VI112-01.10 
PDF · Video · Anti-Windup Disturbance Rejection Control Design for Sampled Systems with Output Delay and Asymmetric Actuator Saturation Constraint (I)

Geng, XinpengDalian University of Technology
Wang, ZhencaiDalian University of Technology
Hao, ShoulinDalian University of Technology
Liu, TaoDalian University of Technology (DLUT)
Nagy, Zoltan K.Loughborough Univ
Keywords: Adaptive observer design, Estimation and filtering
Abstract: A novel anti-windup disturbance rejection control design is proposed for industrial sampled systems with output delay and asymmetric actuator saturation constraint. To deal with the asymmetric actuator saturation constraint as often encountered in engineering practice, the input constraint is equivalently transformed into a symmetric actuator saturation constraint for the convenience of control design. Based on the equivalent system description, a model-based extended state observer (MESO) is designed to simultaneously estimate the system state and disturbance, which becomes an anti-windup compensator when the actuator saturation occurs. In order to compensate for the delay mismatch in MESO, a generalized predictor is utilized to estimate the undelayed system output. Accordingly, a pole placement approach is given to design the feedback controller. A set-point pre-filter is designed to ensure no steady-state output tracking error, in terms of a desired transfer function for the set-point tracking. Based on the delay-dependent sector condition and generalized free-weighting-matrix (GFWM), a suffcient condition guaranteeing the stability of the closed-loop system is established in terms of linear matrix inequalities (LMIs). An illustrative example from the literature is used to demonstrate the effectiveness and advantage of the proposed control method.
Paper VI112-01.11 
PDF · Video · Active Disturbance Rejection Control for Wheeled Mobile Robots with Parametric Uncertainties (I)

Zhu, YichengShanghai Jiao Tong University
Huang, YaoShanghai Jiao Tong University
Su, JianboShanghai Jiaotong Univ
Pu, CuipingKunming University
Keywords: Disturbance rejection, Robust control, Lagrangian and Hamiltonian systems, Application of nonlinear analysis and design
Abstract: In this paper, an efficient controller design method is proposed based on active disturbance rejection control (ADRC) scheme for stabilization problem of wheeled mobile robots with parametric uncertainties, which can make the system converge quickly. By using the extended state observer (ESO), both the system states and the unknown parametric uncertainties could be estimated. In addition, the input-state scaling technique is used to transform the system into two decoupled subsystems. Based on the decoupled subsystems, a switching controller and ADRC are designed. Simulation results show that the proposed scheme can stabilize the wheeled mobile robot system asymptotically despite the presence of parametric uncertainties.
Paper VI112-01.12 
PDF · Video · Discrete Reduced-Order Active Disturbance Rejection Control for Marine Engines Using Variable Sampling Rate Control Scheme under Limited Bandwidth (I)

Wang, RunzhiHarbin Engineering University
Li, XueminHarbin Engineering University
Ma, XiuzhenCollege of Power and Energy Engineering, Harbin Engineering Univ
Keywords: Control architectures in marine systems, Nonlinear and optimal marine system control
Abstract: In this paper, the reduced-order active disturbance rejection control (RADRC) is studied for marine engine speed control. The benefits of using RADRC are demonstrated by bode diagram method with the transfer function between input disturbance and system output. Discrete RADRC and active disturbance rejection control (ADRC) are designed for marine engine speed control by adopting variable sampling rate control method. The proposed method is assessed by experiment on a hard-in-loop (HIL) engine test platform. Except the step-response indexes, ADRC and RADRC are compared in more indexes. The results demonstrate that RADRC has superiority during the sudden load varying process. For steady-state, a single smaller observer bandwidth in RADRC can make a good compromise for a wide range engine speed. It also has been found that the index of total variation (TV) in control input for RADRC is inferior to the ADRC. Overall, RADRC is a promising method for marine engine speed control.
VI112-02
History of Adaptive Control Open Invited Session
Chair: Fradkov, Alexander L.Russian Academy of Sciences
Co-Chair: Polyak, Boris T.Moscow Inst. of Control Sciences
Organizer: Fradkov, Alexander L.Russian Academy of Sciences
Organizer: Giri, FouadUniversity of Caen Normandie
Paper VI112-02.1 
PDF · Video · Speed-Gradient Method in Adaptive Control and Identification. Historical Overview (I)

Andrievsky, BorisInst. for Problems of Mechanical Engineering of the RAS
Pogromsky, A. Yu.Eindhoven Univ of Technology
Plotnikov, SergeiInstitute for Problems of Mechanical Engineering, Russian Academ
Keywords: Nonlinear adaptive control, Nonlinear system identification, Model reference adaptive control
Abstract: The paper provides a historical overview of the Speed-gradient method and its applications to adaptive control and identification problems since mid-1970-th, when the method was originated, till the present days. It is demonstrated that it is an efficient and a useful tool for solving a wide range of engineering problems.
Paper VI112-02.2 
PDF · Video · Adaptive and Robust Control in the USSR (I)

Fradkov, Alexander L.Russian Academy of Sciences
Polyak, Boris T.Moscow Inst. of Control Sciences
Keywords: Nonlinear adaptive control, Machine learning, Extremum seeking and model free adaptive control
Abstract: Control theory in the USSR after WW2 achieved serios successes in such fields as optimal control, absolute stability, delay systems, pulse and relay control. Later there was a huge peak of breakthrough research on adaptation, learning and pattern recognition, starting at 1960th. Next approach to control under uncertainty relates to robustness; the results here are also deep and pioneering. The contributions to all these fields were due to Feldbaum, Aizerman, Lerner, Tsypkin, Yakubovich and their coauthors and colleagues. We try to survey the main stages of this fascinating competition.
Paper VI112-02.3 
PDF · Video · Centennary of Yakov Zalmanovich Tsypkin's Birth (I)

Polyak, Boris T.Moscow Inst. of Control Sciences
Keywords: Nonlinear adaptive control, Estimation and filtering, Identification for control
Abstract: The paper is devoted to the memory of Yakov Zalmanovich Tsypkin and his ``life in feedback control''.
Paper VI112-02.4 
PDF · Video · Notes on Yakubovich’s Method of Recursive Objective Inequalities and Its Application in Adaptive Control and Robotics (I)

Gusev, Sergei V.St. Petersburg State Univ
Bondarko, Vladimir A.St. Petersburg State Univ
Keywords: Learning for control, Nonlinear adaptive control, Identification for control
Abstract: The purpose of the paper is to introduce to the control community the brilliant but little-known part of Yakubovich's academic heritage - the method of recurrent objective inequalities. This method was successfully used by V.A.Yakubovich and his followers in pattern recognition, adaptive control and robotics. The paper deals with the last two topics. The most of surveyed results were published in Russian. A 1975 video about experiments with the first Soviet self-learning robot will be shown.
Paper VI112-02.5 
PDF · Video · Early History of Machine Learning (I)

Fradkov, Alexander L.Russian Academy of Sciences
Keywords: Reinforcement learning control, Knowledge-based control
Abstract: Machine learning belongs to the crossroad of cybernetics (control science) and computer science. It is attracting recently an overwhelming interest, both of professionals and of the general public. In the talk a brief overview of the historical development of the machine learning field with a focus on the development of mathematical apparatus in its first decades is provided. A number of little-known facts published in hard to reach sources are presented.
VI112-03
Iterative Learning Control and Repetitive Control Open Invited Session
Chair: Oomen, TomEindhoven University of Technology
Co-Chair: Tan, YingThe Univ of Melbourne
Organizer: Oomen, TomEindhoven University of Technology
Organizer: Chu, BingUniversity of Southampton
Organizer: Barton, KiraUniversity of Michigan
Organizer: Tan, YingThe Univ of Melbourne
Paper VI112-03.1 
PDF · Video · Iterative Bias Estimation for an Ultra-Wideband Localization System (I)

van der Heijden, BasDelft University of Technology
Ledergerber, AntonETH
Gill, Rajan JoshuaETH
D'Andrea, RaffaelloETH Zurich
Keywords: Adaptive observer design, Iterative and Repetitive learning control, Bayesian methods
Abstract: An iterative bias estimation framework is presented that mitigates position-dependent ranging errors often present in ultra-wideband localization systems. State estimation and control are integrated, such that the positioning accuracy improves over iterations. The framework is experimentally evaluated on a quadcopter platform, resulting in improvements in the tracking performance with respect to ground truth, and also smoothing the overall flight by significantly reducing unwanted oscillations; see https://youtu.be/J-htfbzf40U for a video.
Paper VI112-03.2 
PDF · Video · Output Feedback Based Iterative Learning Control with Finite Frequency Range Specifications Via a Heuristic Approach for Batch Processes with Polytopic Uncertainties (I)

Hao, ShoulinDalian University of Technology
Liu, TaoDalian University of Technology (DLUT)
Paszke, WojciechUniversity of Zielona Gora
Tao, HongfengJiangnan University
Keywords: Iterative and Repetitive learning control, Learning for control
Abstract: For robust control and iterative optimization of industrial batch processes with polytopic uncertainties, this paper proposes a robust output feedback based iterative learning control (ILC) design in terms of finite frequency range stability specifications. Robust stability conditions for the closed-loop ILC system along both time and batch directions are first established based on the generalized Kalman-Yakubovich-Popov lemma and linear repetitive system theory. To facilitate the ILC controller design with respect to process uncertainties described in a polytopic form, extended sufficient conditions for the system stability are then derived in terms of matrix inequalities. Correspondingly, a two-stage heuristic approach is developed to iteratively compute feasible ILC controller gains for implementation. An illustrative example is given to demonstrate the effectiveness of the proposed control design.
Paper VI112-03.3 
PDF · Video · A Model-Free Loop-Shaping Method Based on Iterative Learning Control (I)

Shih, Li-WeiNational Taiwan University
Chen, Cheng-WeiNational Taiwan University
Keywords: Learning for control, Iterative and Repetitive learning control
Abstract: Many techniques have been developed for the loop-shaping method in control design. While most loop-shaping methods apply a model of the open-loop controlled plant, the resulting performance depends on the accuracy of the dynamical model. The aim of this paper is to develop a model-free loop-shaping technique. The core idea is to convert the model matching problem to a trajectory tracking problem. To achieve the desired loop gain, we need to determine the control input such that the system output tracks the impulse response of the loop gain function. In this paper, a model-free iterative learning control (ILC) algorithm is applied to solve this tracking problem. Once the ILC converges, the feedback controller that meets the desired loop gain can then be constructed. This method does not require the model of the controlled plant, hence it provides better performance of loop-shaping control design. The proposed method is validated through numerical simulation on a 3-rd order plant.
Paper VI112-03.4 
PDF · Video · An ILC Approach to Feed-Forward Friction Compensation (I)

Norrlof, MikaelLinköping University
Gunnarsson, SvanteLinkoping University
Keywords: Iterative and Repetitive learning control, Nonlinear system identification, Grey box modelling
Abstract: An iterative, learning based, feed-forward method for compensation of friction in industrial robots is studied. The method is put into an ILC framework by using a two step procedure proposed in literature. The friction compensation method is based on a black-box friction model which is learned from operational data, and this can be seen as the first step in the method. In the second step the learned model is used for compensation of the friction using the reference joint velocity as input. The approach is supported by simulation experiments.
Paper VI112-03.5 
PDF · Video · Improving Mechanical Ventilation for Patient Care through Repetitive Control (I)

Reinders, JoeyEindhoven University of Technology & Demcon Advanced Mechatronic
Verkade, RubenDemcon
Hunnekens, BramEindhoven University of Technology
van de Wouw, NathanEindhoven Univ of Technology
Oomen, TomEindhoven University of Technology
Keywords: Iterative and Repetitive learning control
Abstract: Mechanical ventilators sustain life of patients that are unable to breathe (sufficiently) on their own. The aim of this paper is to improve pressure tracking performance of mechanical ventilators for a wide variety of sedated patients. This is achieved by utilizing the repetitive nature of sedated ventilation through repetitive control. A systematic design procedure of a repetitive controller for mechanical ventilation is presented. Thereafter, the controller is implemented in an experimental setup showing superior tracking performance for a variety of patients.
Paper VI112-03.6 
PDF · Video · Iterative Learning Control and Gaussian Process Regression for Hydraulic Cushion Control (I)

Trojaola, IgnacioIkerlan Technology Research Centre
Elorza, IkerIkerlan
Irigoyen, EloyUniversity of the Basque Country (UPV/EHU)
Pujana-Arrese, AronIKERLAN IK4
Calleja, CarlosIKERLAN
Keywords: Iterative and Repetitive learning control, Nonlinear system identification
Abstract: In this paper, we investigate on extending a feed-forward control scheme for the force control circuit of a hydraulic cushion with Gaussian Process nonlinear regression and Iterative Learning Control. Gaussian Processes allow the possibility of estimating the unknown proportional valve nonlinearities and provide uncertainty measurements of the predictions. However, the system must realize a high precision tracking control which is not achievable if any uncertainty remains in the estimation. Therefore, an extra feed-forward signal based on Iterative Learning Control is used to obtain a precise and fast force reference tracking performance. The design of the Iterative Learning Control is based on an inverted linearized model in which a fourth-order low-pass filter is included to attenuate the unknown valve dynamics. The low-pass filter is split up into two second-order low-pass filters, one of which is applied in the positive, the other in the negative, direction of time, resulting in zero-phase filtering. Simulation results show that Gaussian Process regression allows the possibility of using feed-forward control and that the force tracking performance is improved by introducing Iterative Learning Control.
Paper VI112-03.7 
PDF · Video · Supervised Output Regulation Via Iterative Learning Control for Rejecting Unknown Periodic Disturbances (I)

Kocan, OktayMr
Astolfi, DanieleCNRS - Univ Lyon 1
Poussot-Vassal, CharlesOnera
Manecy, AugustinOnera
Keywords: Iterative and Repetitive learning control, Learning for control, Filtering and smoothing
Abstract: The internal model principle (IMP) in linear robust output regulation theory states that a dynamical controller needs to incorporate a copy of the model generating the periodic signals in order to achieve perfect rejection/tracking, robustly with respect to plant's parameters. On the other hand Iterative Learning Control (ILC) is a data-based approach which not requires any a priori knowledge, and can be used to find the required control action for attenuating periodic disturbances or tracking periodic references. The control signal generated by ILC includes the frequency and amplitude information of the disturbance and can be used to build the internal model needed for a linear output regulator problem. The objective of this work is therefore that of trying to combine the two approaches, that is IMP and ILC, in order to retain the advantages of each methodology. The proposed methodology, denoted as Supervised Output Regulation via Iterative Learning Control (SOR-ILC), allows to address the problem of output regulation in presence of unknown frequencies. The performances of SOR-ILC are validated through numerical simulations in case of complex periodic disturbances and parameter uncertainties.
Paper VI112-03.8 
PDF · Video · On Improving Transient Behavior and Steady-State Performance of Model-Free Iterative Learning Control (I)

Zhang, Geng-HaoNational Taiwan University
Chen, Cheng-WeiNational Taiwan University
Keywords: Iterative and Repetitive learning control, Recursive identification, Time series modelling
Abstract: A novel model-free iterative learning control algorithm is proposed in this paper to improve both the robustness against output disturbances and the tracking performance in steady-state. For model-free ILC, several methods have been investigated, such as the time-reversal error filtering, the Model-Free Inversion-based Iterative Control (MFIIC), and the Non-Linear Inversion-based Iterative Control (NLIIC). However, the time-reversal error filtering has a conservative learning rate. Other two methods, although with much faster error convergence, have either a high noise sensitivity or a non-optimized steady-state. To improve the performance and robustness of model-free ILC, we apply the time-reversal based ILC and recursively accelerate its error convergence using the online identified learning filter. The effectiveness of the proposed algorithm has been validated by a numerical simulation. The proposed approach not only improves the transient response of the MFIIC, but achieves lower tracking error in steady-state compared to that of the NLIIC.
Paper VI112-03.9 
PDF · Video · Iterative Learning Control for Output Tracking of Systems with Unmeasurable States (I)

Li, XuefangSun Yat-Sen University
Shen, DongRenmin University of China
Keywords: Iterative and Repetitive learning control
Abstract: In this work, a new design framework of adaptive iterative learning control (ILC) approach for a class of uncertain nonlinear systems is presented. By making use of the closed-loop reference model which works as an observer, the developed adaptive ILC method is able to be adopted to deal with the output tracking problem of nonlinear systems without requiring the measurability of system states. In the system, the uncertainties are formed by the product of unknown parameters and state functions that are also unknown as the system states are not available. In order to facilitate the controller design and convergence analysis, the composite energy function (CEF) method is employed, and the accurate tracking task can be realized successfully. The proposed approach extends CEF-based ILC approach sucessfully to output tracking control of nonlinear systems without requiring the system states information and complicated observer design. The effectiveness of the proposed ILC scheme is verified through an illustrative numerical example.
Paper VI112-03.10 
PDF · Video · Iterative Learning Control for Switched Systems in the Presence of Input Saturation (I)

Pakshin, PavelArzamas Polytechnic Institute of R.E. Alekseev NSTU
Emelianova, JuliaArzamas Polytechnic Institute of R.E. Alekseev NSTU
Rogers, EricUniv of Southampton
Galkowski, KrzysztofUniv. of Zielona Gora
Keywords: Iterative and Repetitive learning control, Learning for control, Hybrid and switched systems modeling
Abstract: The paper considers iterative learning control for differential and discrete switched linear systems with control input saturation. A new design is developed based on the use of common vector Lyapunov functions. An example demonstrating the features and advantages of the new design is given.
Paper VI112-03.11 
PDF · Video · On the Role of Models in Learning Control: Actor-Critic Iterative Learning Control (I)

Poot, MauriceEindhoven University of Technology
Portegies, JimEindhoven University of Technology
Oomen, TomEindhoven University of Technology
Keywords: Learning for control, Consensus and Reinforcement learning control, Iterative and Repetitive learning control
Abstract: Learning from data of past tasks can substantially improve the accuracy of mechatronic systems. Often, for fast and safe learning a model of the system is required. The aim of this paper is to develop a model-free approach for fast and safe learning for mechatronic systems. The developed actor-critic iterative learning control (ACILC) framework uses a feedforward parameterization with basis functions. These basis functions encode implicit model knowledge and the actor-critic algorithm learns the feedforward parameters without explicitly using a model. Experimental results on a printer setup demonstrate that the developed ACILC framework is capable of achieving the same feedforward signal as preexisting model-based methods without using explicit model knowledge.
Paper VI112-03.12 
PDF · Video · Stability of Switched Differential Repetitive Processes and Iterative Learning Control Design (I)

Emelianova, JuliaArzamas Polytechnic Institute of R.E. Alekseev NSTU
Pakshin, PavelArzamas Polytechnic Institute of R.E. Alekseev NSTU
Emelianov, MikhailArzamas Polytechnic Institute of R.E. Alekseev NSTU
Keywords: Iterative and Repetitive learning control, Stability and stabilization of hybrid systems, Learning for control
Abstract: In this paper general stability conditions of differential nonlinear repetitive processes with switching are obtained. The approach is based on the development of a method that uses vector Lyapunov functions and the properties of the counterpart of its divergence. The obtained results are applied to iterative learning control design for switched linear system. An example that demonstrate effectiveness of the new design is given.
Paper VI112-03.13 
PDF · Video · New Relaxed Stability and Stabilization Conditions for Differential Linear Repetitive Processes (I)

Maniarski, RobertUniversity of Zielona Góra
Paszke, WojciechUniversity of Zielona Gora
Rogers, EricUniv of Southampton
Boski, MarcinFaculty of Computer, Electrical and Control Engineering Universi
Keywords: Iterative and Repetitive learning control
Abstract: The paper develops new results on the stability analysis of differential linear repetitive processes. These processes are a distinct class of two-dimensional (2D) systems that arise in the modelling of physical processes and also the existing systems theory for them can be used to effect in solving control problems for other classes of systems, including iterative learning control design. This paper uses a version of the Kalman-Yakubovich-Popov Lemma to develop relaxed conditions for the stability property in terms of linear matrix inequalities (LMIs). The main result is reduced conservatism in applying tests for the stability property with an extension to state feedback control law design. The numerical example of a metal rolling process is given to support the new results.
Paper VI112-03.14 
PDF · Video · Repetitive Control of Nonlinear Systems Via Feedback Linearization: An Application to Robotics (I)

Biagiotti, LuigiUniversity of Modena and Reggio Emilia
Keywords: Iterative and Repetitive learning control
Abstract: In this paper, a novel Repetitive Control (RC) scheme for a class of nonlinear systems is presented and discussed. This work generalizes the approach proposed in Biagiotti et al. (2015) where a RC scheme based on the modification of a B-spline reference trajectory has been presented. Also in this case, the generation of the B-splines based on dynamic filters plays a crucial role in the control scheme since it allows to implement a feedforward action that, coupled with an exact feedback linearization and a stabilizing state feedback, makes the RC robustly asymptotically stable. In this manner, the tracking error at the via-points defining the reference trajectory is nullified even if parametric uncertainties on the system model or exogenous (cyclic) disturbances are present. The application to a two-dof robot manipulator shows the effectiveness of the proposed method and its inherent robustness.
Paper VI112-03.15 
PDF · Video · Monotonically Convergent Iterative Learning Control for Piecewise Affine Systems (I)

Strijbosch, NardEindhoven University of Technology
Spiegel, IsaacUniversity of Michigan
Barton, KiraUniversity of Michigan
Oomen, TomEindhoven University of Technology
Keywords: Iterative and Repetitive learning control, Learning for control, Hybrid and switched systems modeling
Abstract: Piecewise affine (PWA) systems enable modelling of systems that encompass hybrid dynamics and nonlinear effects. The aim of this paper is to develop an ILC framework for PWA systems. A new approach to analyse monotonic convergence is developed for PWA systems. This is achieved by exploiting the incremental l2-gain leading to sufficient LMI conditions guaranteeing monotonic convergence. An example confirms the monotonic convergence property for ILC applied to a mass-spring-damper system with a one-sided spring.
Paper VI112-03.16 
PDF · Video · Overcoming Output Constraints in Iterative Learning Control Systems by Reference Adaptation (I)

Meindl, MichaelHochschule Karlsruhe - Technik Und Wirtschaft
Molinari, FabioTechnische Universitaet Berlin
Raisch, JoergTechnische Universitaet Berlin
Seel, ThomasTechnische Universitaet Berlin
Keywords: Iterative and Repetitive learning control, Learning for control
Abstract: Iterative Learning Control (ILC) schemes can guarantee properties such as asymptotic stability and monotonic error convergence, but do not, in general, ensure adherence to output constraints. The topic of this paper is the design of a reference-adapting ILC (RAILC) scheme, extending an existing ILC system and capable of complying with output constraints. The underlying idea is to scale the reference at every trial by using a conservative estimate of the output’s progression. Properties as the monotonic convergence above a threshold and the respect of output constraints are formally proven. Numerical simulations and experimental results reinforce our theoretical results.
Paper VI112-03.17 
PDF · Video · Gaussian Process Repetitive Control for Suppressing Spatial Disturbances (I)

Mooren, NoudEindhoven University of Technology
Witvoet, GertTNO
Oomen, TomEindhoven University of Technology
Keywords: Iterative and Repetitive learning control, Learning for control, Machine learning
Abstract: Motion systems are often subject to disturbances such as cogging, commutation errors, and imbalances, that vary with velocity and appear periodic in time for constant operating velocities. The aim of this paper is to develop a repetitive controller (RC) for disturbances that are not periodic in the time domain, yet occur due to an identical position-domain disturbance. A new spatial RC framework is developed, allowing to attenuate disturbances that are periodic in the position domain but manifest a-periodic in the time domain. A Gaussian process (GP) based memory is employed with a suitable periodic kernel that can effectively deal with the intermittent observations inherent to the position domain. A mechatronic example confirms the potential of the method.
Paper VI112-03.18 
PDF · Video · Switch-Based Iterative Learning Control for Tracking Iteration Varying References (I)

Balta, EfeUniversity of Michigan
Tilbury, Dawn M.Univ of Michigan
Barton, KiraUniversity of Michigan
Keywords: Iterative and Repetitive learning control, Adaptive gain scheduling autotuning control and switching control, Learning for control
Abstract: Iterative Learning Control (ILC) is a control strategy that improves the performance of repetitive systems by enabling near-perfect reference tracking. Iteration-invariant reference signals have been a fundamental assumption for most existing ILC developments. This assumption poses limitations on many applications of ILC where the iteration-varying reference is known to the controller a priori. This work presents a switch-based ILC scheme that combines the performance of standard ILC with guarantees on the error for switched reference signals. The proposed controller is formulated and its performance is analyzed. A simulation case study is provided at the end to illustrate the performance.
Paper VI112-03.19 
PDF · Video · Efficient Implementation of a Binary Iterative Learning Control

Arnold, FlorianTechnische Universität Berlin
Topalovic, DanielTechnische Universität Berlin
King, RudibertTechnische Universitaet Berlin
Keywords: Iterative and Repetitive learning control
Abstract: Iterative learning control (ILC) is an adequate control approach to handle various types of cyclic control tasks. However, when in each iteration the calculation of the control trajectory requires the solution of a high dimensional constrained quadratic program, the algorithm is bound to be infeasible for real-time applications with very small cycle lengths in the order of milliseconds due to the prohibitively large computational cost. In this contribution, an approach is presented to reduce the computational burden to solve an optimization-based iterative learning control that is restricted to a binary domain by orders of magnitude. The method is suitable for control trajectories that contain only few 1’s, but a large number of 0’s in each iteration for a specific class of problems, e.g., for cyclic firing synchronization of combustion tubes such is required. The presented setup is tested experimentally at an acoustic mock-up of an annular pulse detonation combustor to determine an appropriate fire synchronization. More specifically, it is used to adjust the firing pattern of multiple simulated combustion tubes in order to reduce pressure fluctuations measured downstream in an annular plenum, which is a prerequisite to apply such a new thermodynamically efficient combustion process in a real gas turbine.
Paper VI112-03.20 
PDF · Video · Disturbance Observer Based Repetitive Control System with Non-Minimal State Space Realization and Anti-Windup Mechanism

Wang, LiupingRMIT University
Freeman, Christopher ThomasUniversity of Southampton
Rogers, EricUniv of Southampton
Young, PeterLancaster University
Keywords: Iterative and Repetitive learning control
Abstract: This paper develops a disturbance observer-based repetitive control system using a non-minimal state-space realization in which all state variables are chosen to correspond to the system's input and output variables and their past values. To enable the repetitive control system to follow a periodic reference signal or reject a disturbance signal of the same nature, a disturbance observer is used to estimate an input disturbance that contains the same frequency characteristics. This new approach differs from previously published design in repetitive control because it separates the design procedure into two simple tasks: first, stabilization by the design of a non-minimal state feedback control; and second, to independently incorporate the periodic modes via the estimation of the disturbance. Moreover, because this design ensures the stability of the disturbance observer, its implementation contains an anti-windup mechanism when the control signal reaches its maximum or minimum value. Without the complication of an observer for the state variables, the detection of a disturbance occurs earlier and the repetitive controller acts much faster than in the case of minimal state controller incorporating an observer. This leads to considerable performance improvement, with excellent disturbance rejection achieved with smaller control signal variations.
Paper VI112-03.21 
PDF · Video · Application of the Dynamic Iterative Learning Control to the Heteropolar Active Magnetic Bearing

Hladowski, LukaszUniversity of Zielona Gora
Mystkowski, ArkadiuszBialystok University of Technology
Galkowski, KrzysztofUniv. of Zielona Gora
Rogers, EricUniv of Southampton
Chu, BingUniversity of Southampton
Keywords: Regulation (linear case), Linear systems, Time-invariant systems
Abstract: Heteroplanar active magnetic bearings have numerous applications, where one example is a high-temperature gas-cooled reactors. Rotor imbalance, however, may cause problems for critical parts of the system in the form of repetitive periodic vibrations. This is known problem and periodic component extraction is widely used in active magnetic bearing unbalance control laws. More recently, iterative learning control has been considered as an alternative and this paper gives new results on this approach. In particular, a new control law in the 2D systems setting is developed and the results of a simulation based study using the model of a test rig are given, where such a study is an essential step prior to experimental validation.
VI112-04
Adaptive Observers and Control Regular Session
Chair: Chen, SonglinHarbin Institute of Technology
Co-Chair: Bullinger, EricOtto-von-Guericke-Universität Magdeburg
Paper VI112-04.1 
PDF · Video · Optimal Model-Based Sensor Placement & Adaptive Monitoring of an Oil Spill

Hodgson, ZakUniversity of Sheffield
Esnaola, IñakiUniversity of Sheffield
Jones, Bryn L.University of Sheffield
Keywords: Adaptive control of multi-agent systems, Multi-agent systems, Control under computation constraints
Abstract: This paper presents a model based adaptive monitoring method for the estimation of flow tracers, with application to mapping, prediction and observation of oil spills in the immediate aftermath of an incident. Autonomous agents are guided to optimal sensing locations via the solution of a PDE constrained optimisation problem, obtained using the adjoint method. The proposed method employs a dynamic model of the combined ocean and oil dynamics, with states that are updated in real-time using a Kalman filter that fuses agent-based measurements with a reduced-order model of the ocean circulation dynamics. In turn, the updated predictions from the fluid model are used to identify and update the reduced order model, in a process of continuous feedback. The proposed method exhibits a 30% oil presence mapping and prediction improvement compared to standard industrial oil observation sensor guidance and model use.
Paper VI112-04.2 
PDF · Video · Transforming Time-Delay System Observers to Adaptive Observers

Ahmed-Ali, TarekUniversité De Caen Normandie
Zhang, QinghuaINRIA
Giri, FouadUniversity of Caen Normandie
Liu, XingwenSouthwest University for Nationalities of China
Keywords: Adaptive observer design
Abstract: For joint estimation of states and parameters in time varying time-delay systems (TDS) involving both distributed and lumped time-delays, a general approach is proposed in this paper to transforming existing (non adaptive) observers to adaptive observers. In addition to the convergence conditions of the considered existing observers, a persistent excitation condition is introduced in order to ensure the convergence of parameter estimation. In contrast to implicitly formulated convergence conditions, which are usually assumed jointly for both state and parameter estimations in most TDS adaptive observers, the persistent excitation condition in the proposed approach is explicitly formulated and decoupled from the conditions initially assumed for state estimation.
Paper VI112-04.3 
PDF · Video · Fixed-Time Estimators of Derivatives of Unknown Maps (I)

Wang, LibinHarbin Institute of Technology
Chen, SonglinHarbin Institute of Technology
Krstic, MiroslavUniv. of California at San Diego
Zhao, HuiHarbin Institute of Technology
Keywords: Adaptive observer design
Abstract: A systematic and generalized asymptotic derivative estimator design method is first presented for unknown maps by adding a sinusoidal excitation signal to the argument of the map. Then, based on the proposed asymptotic derivative estimator approach and based on the existing design methods for both finite-time and fixed-time state observers, finite-time and fixed-time derivative estimators are designed. The sufficient conditions for finite-time and fixed-time input-to-state stable of the finite-time and fixed-time derivative estimators are given respectively when a bounded disturbance input exists.
Paper VI112-04.4 
PDF · Video · A Robust Sensorless Controller-Observer Strategy for PMSMs with Unknown Resistance and Mechanical Model

Bosso, AlessandroAlma Mater Studiorum - University of Bologna
Tilli, AndreaUniversity of Bologna
Conficoni, ChristianAlma Mater Studiorum Bologna
Keywords: Adaptive observer design, Closed loop identification, Continuous time system estimation
Abstract: In this work, we present a mixed sensorless strategy for Permanent Magnet Synchronous Machines, combining a torque/current controller and an observer for position, speed, flux, and stator resistance. The proposed co-design is motivated by the need for an appropriate signal injection technique to guarantee full state observability. Neither the typical constant or slowly-varying speed assumptions, nor a priori mechanical model information are required. Instead, the rotor speed is modeled as an unknown input disturbance with constant (unknown) sign and uniformly non-zero magnitude. With the proposed architecture, we show that the torque tracking and signal injection tasks can be achieved and asymptotically decoupled. Because of these features, we refer to this strategy as a sensorless controller-observer with no mechanical model. Employing a gradient descent resistance/back-EMF estimation, combined with the unit circle formalism to describe the rotor position, we prove regional practical asymptotic stability of the overall scheme. In particular, the domain of attraction can be arbitrarily large, without including a lower-dimensional manifold. The effectiveness of this design is further validated with numerical simulations, related to a challenging application of UAV propellers control.
Paper VI112-04.5 
PDF · Video · Mixed Fractional Order Adaptive Control: Theory and Applications

Duarte-Mermoud, ManuelUniv of Chile
Barzaga Martell, LisbelUnivesity of Chile
Ceballos Benavides, GustavoUniversidad San Sebastián
Keywords: Model reference adaptive control
Abstract: In this paper we study the adaptive control problem of integer order plants using fractional order adaptive laws in the controller. The study is based on a general methodology recently developed to establish boundeness and asymptotic behavior of solutions to multiorder systems (set of differential equations with different derivation orders) having multiple time-varying delays. Also it is based on recent results for fractional order systems under the perspective of the so called "Error Models". The method relies on vector Lyapunov-like functions and on comparison arguments. Boundedness and convergence of the solutions are theoretically analyzed and applications to fractional adaptive schemes are presented towards the end of the paper, including numerical simulations to verify the analytical results.
VI112-05
Consensus and Reinforcement Learning Control Regular Session
Chair: Basar, TamerUniv. of Illinois at Urbana-Champaign
Co-Chair: Stankovic, Milos S.University of Belgrade
Paper VI112-05.1 
PDF · Video · A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement Learning

Suttle, WesleyStony Brook University
Yang, ZhuoranPrinceton
Zhang, KaiqingUniversity of Illinois at Urbana-Champaign (UIUC)
Wang, ZhaoranNorthwestern University
Basar, TamerUniv. of Illinois at Urbana-Champaign
Liu, JiStony Brook University
Keywords: Consensus and Reinforcement learning control, Adaptive control of multi-agent systems
Abstract: This paper extends off-policy reinforcement learning to the multi-agent case in which a set of networked agents communicating with their neighbors according to a time-varying graph collaboratively evaluates and improves a target policy while following a distinct behavior policy. To this end, the paper develops a multi-agent version of emphatic temporal difference learning for off-policy policy evaluation, and proves convergence under linear function approximation. The paper then leverages this result, in conjunction with a novel multi-agent off-policy policy gradient theorem and recent work in both multi-agent on-policy and single-agent off-policy actor-critic methods, to develop and give convergence guarantees for a new multi-agent off-policy actor-critic algorithm. An empirical validation of these theoretical results is given.
Paper VI112-05.2 
PDF · Video · Deep Decentralized Reinforcement Learning for Cooperative Control

Koepf, FlorianKarlsruhe Institute of Technology (KIT)
Tesfazgi, SamuelTechnical University Munich
Flad, MichaelKarlsruhe Institute of Technology
Hohmann, SoerenKIT
Keywords: Consensus and Reinforcement learning control, Adaptive control of multi-agent systems, Learning for control
Abstract: In order to collaborate efficiently with unknown partners in cooperative control settings, adaptation of the partners based on online experience is required. The rather general and widely applicable control setting where each cooperation partner might strive for individual goals while the control laws and objectives of the partners are unknown entails various challenges such as the non-stationarity of the environment, the multi-agent credit assignment problem, the alter-exploration problem and the coordination problem. We propose new, modular deep decentralized Multi-Agent Reinforcement Learning mechanisms to account for these challenges. Therefore, our method uses a time-dependent prioritization of samples, incorporates a model of the system dynamics and utilizes variable, accountability-driven learning rates and simulated, artificial experiences in order to guide the learning process. The effectiveness of our method is demonstrated by means of a simulated nonlinear cooperative control task.
Paper VI112-05.3 
PDF · Video · Distributed Gradient Temporal Difference Off-Policy Learning with Eligibility Traces: Weak Convergence

Stankovic, Milos S.University of Belgrade
Beko, MarkoCOPELABS, Universidade Lusofona De Humanidades E Tecnologias, Li
Stankovic, SrdjanUniversity of Belgrade
Keywords: Consensus and Reinforcement learning control, Distributed control and estimation, Multi-agent systems
Abstract: In this paper we propose two novel distributed algorithms for multi-agent off-policy learning of linear approximation of the value function in Markov decision processes. The algorithms differ in the way of how distributed consensus iterations are incorporated in a basic, recently proposed, single agent scheme. The proposed completely decentralized off-policy learning schemes subsume local eligibility traces, and allow applications in which all the agents may have different behavior policies while evaluating a single target policy. Under nonrestrictive assumptions on the time-varying network topology and the individual state-visiting distributions of the agents, we prove that the parameter estimates of the algorithms weakly converge to a consensus. The variance reduction properties of the proposed algorithms are demonstrated. We also formulate specific guidelines on how to design the network weights and topology. The results are illustrated using simulations.
Paper VI112-05.4 
PDF · Video · Actuation Strategy of a Virtual Skydiver Derived by Reinforcement Learning

Clarke, AnnaTechnion Israel Institute of Technology
Gutman, Per-OlofTechnion - Israel Institute of Technology
Keywords: Consensus and Reinforcement learning control, Learning for control, Experiment design
Abstract: An innovative approach of training motor skills involved in human body flight is proposed. Body flight is the art of maneuvering during the free fall stage of skydiving. The key idea is gradually constructing the movement patterns which are the combinations of body degrees-of-freedom that are activated synchronously and proportionally as a single unit, and turning this process into a coaching strategy. The proposed method is iterative: at each skill level an optimal movement pattern is constructed from the basic elements of the current movement repertoire. The free-fall maneuvers of each learning stage can be executed using any one of the basic elements. The construction has two stages: 1. tracking the desired maneuver while the body is actuated by each one of the basic patterns; 2. finding an optimal combination of these patterns to form a new way of body actuation. This hierarchical design resolves stage 2 by Reinforcement Learning with pure exploration and a minimal number of episodes. The method was tested in a Skydiver Simulator and resulted in deriving a movement pattern that showed a superior performance of the studied maneuver. The states and the reward of the Reinforcement Learning algorithm were converted into motor learning aids.
Paper VI112-05.5 
PDF · Video · Model-Free Control Design for Loop Heat Pipes Using Deep Deterministic Policy Gradient

Gellrich, ThomasFZI Research Center for Information Technology
Min, YiFZI Research Center for Information Technology
Schwab, StefanFZI - Research Center for Information Technology
Hohmann, SoerenKIT
Keywords: Consensus and Reinforcement learning control, Learning for control, Extremum seeking and model free adaptive control
Abstract: In this paper, a model-free adaptive control design for loop heat pipes (LHPs) based on the reinforcement learning (RL) method of deep deterministic policy gradient (DDPG) is presented. An LHP as a heat transport system combines complex, thermodynamic processes, which are not yet fully described in a dynamic control model over the entire LHP operating range for model-based control design. However, RL methods provide the controller with the ability to improve its control performance without a model by analyzing and rewarding the performance online. The aim of an LHP controller is to keep the LHP operating temperature as close as possible to the fixed setpoint temperature by additional heating, while the amount of heat to be transported and the temperature of the heat sink change over time. A validated numerical simulation of the LHP provides a safe, dynamic environment for the training of the learning controller. In comparison with the commonly used PI controller with a single temperature feedback, the control performance of the learning controller observing the same temperature achieves similar control results. Furthermore, multiple observations are easily incorporated into a model-free learning controller, whereby the additional feedback of further temperature measurements ensures an improved performance over the entire operating range.
Paper VI112-05.6 
PDF · Video · Reinforcement Learning and Trajectory Planning Based on Model Approximation with Neural Networks Applied to Transition Problems

Pritzkoleit, MaxTU Dresden
Knoll, CarstenTechnische Universität Dresden
Röbenack, KlausTU Dresden
Keywords: Consensus and Reinforcement learning control, Learning for control, Nonlinear system identification
Abstract: In this paper we use a multilayer neural network to approximate the dynamics of nonlinear (mechanical) control systems. Furthermore, these neural network models are combined with offline trajectory planning, to form a model-based reinforcement learning (RL) algorithm, suitable for transition problems of nonlinear dynamical systems. We evaluate the algorithm on the swing-up of the cart-pole benchmark system and observe a significant performance gain in terms of data efficiency compared to a state-of-the-art model-free RL method (Deep Deterministic Policy Gradient (DDPG)). Additionally, we present first experimental results on a cart-triple-pole system test bench. For a simple transition problem, the proposed algorithm shows a good controller performance.
Paper VI112-05.7 
PDF · Video · Automatic Exploration Process Adjustment for Safe Reinforcement Learning with Joint Chance Constraint Satisfaction

Okawa, YoshihiroFujitsu Laboratories Ltd
Sasaki, TomotakeFujitsu Laboratories Ltd
Iwane, HidenaoReading Skill Test, Inc
Keywords: Consensus and Reinforcement learning control, Nonlinear adaptive control, Learning for control
Abstract: In reinforcement learning (RL) algorithms, exploratory control inputs are used during learning to acquire knowledge for decision making and control, while the true dynamics of a controlled object is unknown. However, this exploring property sometimes causes undesired situations by violating constraints regarding the state of the controlled object. In this paper, we propose an automatic exploration process adjustment method for safe RL in continuous state and action spaces utilizing a linear nominal model of the controlled object. Specifically, our proposed method automatically selects whether the exploratory input is used or not at each time depending on the state and its predicted value as well as adjusts the variance-covariance matrix of a normal distribution used in the Gaussian policy for exploration. We also show that our exploration process adjustment method theoretically guarantees the satisfaction of the constraints with the pre-specified probability, that is, the satisfaction of a joint chance constraint at every time. Finally, we illustrate the validity and the effectiveness of our method through numerical simulation.
Paper VI112-05.8 
PDF · Video · Online Output-Feedback Optimal Control of Linear Systems Based on Data-Driven Adaptive Learning

Zhao, JunKunming University of Science and Technology
Na, JingUniversity of Bristol
Gao, GuanbinKunming University of Science & Technology
Han, ShichangKunming University of Science and Technology
Chen, QiangZhejiang University of Technology
Wang, ShuboQingdao University
Keywords: Consensus and Reinforcement learning control, Nonlinear adaptive control, Neural and fuzzy adaptive control
Abstract: This paper proposes a new approach to solve the output-feedback optimal control for linear systems. A modified algebraic Riccati equation (MARE) is constructed by investigating the corresponding relationship with the state-feedback optimal control. To solve the derived MARE, an online data-driven adaptive learning is designed, where the vectorization operation and Kronecker’s product are applied to reformulate the output Lyapunov function. Consequently, only the measurable system input and output are used to derive the solution of the MARE. In this case, the output-feedback optimal control solution can be obtained in an online manner without resorting to the unknown system states. Simulation results are provided to demonstrate the efficacy of the suggested method.
Paper VI112-05.9 
PDF · Video · Learning Optimal Switching Feedback Controllers from Data

Ferrarotti, LauraIMT School for Advanced Studies, Lucca
Bemporad, AlbertoIMT Institute for Advanced Studies Lucca
Keywords: Consensus and Reinforcement learning control, Optimal control of hybrid systems, Machine learning
Abstract: In this paper we present a data-driven approach for synthesizing optimal switching controllers directly from experimental data, without the need of a global model of the dynamics of the process. The set of controllers and the switching law are learned by using a coordinate descent strategy: for a fixed switching law, the controllers are sequentially optimized by using stochastic gradient descent iterations, while for fixed controllers the switching law is iteratively refined by unsupervised learning. We report examples showing that the approach performs well when applied to control processes characterized by hybrid or nonlinear dynamics, outperforming control laws that are single-mode (no switching) or multi-mode but with the switching law defined a priori.
VI112-06
Extremum Seeking and Model-Free Adaptive Control Regular Session
Chair: Ebenbauer, ChristianUniversity of Stuttgart
Co-Chair: Marconi, LorenzoUniv. Di Bologna
Paper VI112-06.1 
PDF · Video · Discrete-Step, Quasi-Newton Extremum Seeking Control for Multivariate Real-Time Optimization (I)

Lange, AndreasTechnische Universitaet Berlin
King, RudibertTechnische Universitaet Berlin
Keywords: Extremum seeking and model free adaptive control
Abstract: Extremum seeking control is a well known approach for multivariate real-time optimization of dynamic systems. In classical extremum seeking control schemes, the estimated gradient of the process's steady-state map is continuously integrated towards a local optimum. Gradient estimation can be done by a combination of low- and high-/bandpass filters. Advanced approaches have been developed that use extended Kalman Filters, which allow for a joined estimation of the multidimensional gradient. Within this work, a discrete-step real-time optimization scheme is investigated that is derived from the prevalent quasi-Newton method of numerical optimization. Gradient estimation is implemented by the identification of a local linear dynamic model. A significantly faster convergence to the optimum compared to classical extremum seeking is shown for an academic Hammerstein example and for the optimization of the power consumption within a multistage compressor simulation.
Paper VI112-06.2 
PDF · Video · Real-Time Efficient Operation of Decatizing Processes Via a Geometric-Based Extremum Seeking Control

Ferro, Fabiana FedericaUniversity of Padova
Lionello, MicheleUniversity of Padova
Rampazzo, MircoUniversita Degli Studi Di Padova
Beghi, AlessandroUniversità Di Padova
Guay, MartinQueen's Univ
Keywords: Extremum seeking and model free adaptive control
Abstract: Finishing is one of the fundamental steps of textile production and still, nowadays, it largely depends on empirical knowledge. Aim of finishing processes is to impart the required functional properties to the fabric and, in particular, decatizing is the process that lends the fabrics dimensional stability, enhances the luster, and improves the so-called ‘fabric hand’, corresponding to the sense of touching a textile. In order to properly treat the textile while minimizing the process energy consumption, suitable decatizing operating conditions, such as air temperature and humidity in the steaming section, have to be set. In this paper, because of the limited knowledge of certain process parameters and the difficulty of developing, implementing, and using effective a-priori process models, the problem of determining the set-points for the main controlled process variables is formulated as a constrained optimization problem and this is faced by means of a model-free approach. More precisely, we consider a constrained Extremum Seeking Control scheme, using a geometric approach. In this preliminary study, first, we develop a Matlab-based simulation environment for the textile decatizing process and then we exploit it to design and test the ESC scheme. The in silico results confirm the effectiveness of the proposed approach.
Paper VI112-06.3 
PDF · Video · Extremum Seeking Control Based on the Super-Twisting Algorithm

Torres, IxbalankUniversidad De Guanajuato
Lopez-Caamal, FernandoUniversidad De Guanajuato
Hernández-Escoto, HéctorUniversity of Guanajuato
Vargas, AlejandroInstituto De Ingenieria UNAM
Keywords: Real-time optimal control, Robust control applications, Sliding mode control
Abstract: This article addresses the problem of extremum seeking of a continuous-time dynamical system with a single input and a single output. First, a super-twisting-based gradient-based optimization algorithm is proposed to compute the input that leads to the extremum value of an unknown, convex objective function. Since the algorithm requires the input-output gradient of the system's response, a super-twisting based differentiator is proposed to compute the gradient using the measured output and the controlled input. Feasibility of the extremum seeking controller is demonstrated via closed-loop simulations over a microalgae production photobioreactor.
Paper VI112-06.4 
PDF · Video · An Extremum Seeking Approach to Search and Rescue Operations in Avalanches Using ARVA

Azzollini, Ilario AntonioUniversity of Bologna
Mimmo, NicolaUniversity of Bologna
Marconi, LorenzoUniv. Di Bologna
Keywords: Extremum seeking and model free adaptive control
Abstract: Search and rescue operations in avalanches can greatly benefit from the support of unmanned aerial vehicles, which could safely and autonomously fly above the snow surface to estimate the position of the victim. This work relies upon the Appareil de Recherche de Victimes (ARVA), which consists of a transmitter and a receiver. The transmitter is worn by the victim and produces an electromagnetic field that can be sensed by the receiver, integrated on the drone. A receiver able to sense the complete 3D electromagnetic field has been developed, whose model and properties are presented in this work. The main contribution of this work is the development of a control algorithm able to drive the ARVA-equipped drone as close as possible to the victim location.
Paper VI112-06.5 
PDF · Video · Dither Signals Optimization in Constrained Multi-Agent Extremum Seeking Control

Silva, Thiago L.Norwegian University of Science and Technology
Pavlov, AlexeyNorwegian University of Science and Technology
Keywords: Extremum seeking and model free adaptive control, Multi-agent systems
Abstract: In this paper we consider the problem of optimization of a multi-agent system with constraints through perturbations-based extremum seeking control. We demonstrate that for such systems, effects of dither signals applied to individual agents can sum up to significant perturbations in the outputs at the overall system level despite the fact that individual dither signals can be small. These perturbations are especially detrimental in constrained outputs. To resolve this challenge, we propose a method of dither signals optimization: while maintaining persistent perturbations of individual agents, dither signals are coordinated between the agents to minimize their summed effect in constrained outputs. This problem is formulated as a computationally feasible mathematical programming problem that can be solved numerically at each time step. Combined with a constrained steady-state optimizer and a least squares-based gradient estimator, this method provides better performance than a similar perturbation-based extremum seeking scheme without dither optimization. This is demonstrated with an example on oil production optimization from a system of multiple gas-lifted wells with a total water processing constraint.
Paper VI112-06.6 
PDF · Video · Quantized Measurements in Q-Learning Based Model-Free Optimal Control

Tiistola, Sini PäivikkiTampere University
Ritala, RistoTampere University of Technology, Dept of Automation Science And
Vilkko, Matti KalervoTampere University
Keywords: Extremum seeking and model free adaptive control, Quantized systems, Machine learning
Abstract: Quantization noise is present in many real-time applications due to the resolution of analog-to-digital conversions. This can lead to error in policies that are learned by model-free Q-learning. A method for quantization error reduction for Q-learning algorithms is developed using the sample time and an exploration noise that is added to the control input. The method is illustrated with discrete-time policy and value iteration algorithms using both a simulated environment and a real-time physical system.
Paper VI112-06.7 
PDF · Video · Discrete-Time Repetitive Control for Multi-Harmonic Reference Trajectories with Arbitrary Frequency

Marko, LukasVienna University of Technology
Saxinger, MartinVienna University of Technology
Bittner, MatthiasVienna University of Technology
Steinboeck, AndreasVienna University of Technology
Kugi, AndreasVienna University of Technology
Keywords: Iterative and Repetitive learning control, Extremum seeking and model free adaptive control
Abstract: In this work, a repetitive control approach for the tracking of harmonic reference trajectories in the presence of actuator backlash and sticking friction is presented. A spatial Fourier series formulation is utilized to obtain a learning law which is independent of the desired reference frequency. Subsequently, discrete-time averaging is employed, which results in a simple convergence criterion for the closed-loop system. Furthermore, all updates are calculated in a time-recursive manner, which avoids the necessity of large data windows and allows for a discrete-time implementation with a uniform sampling time. Finally, experimental results of a fully assembled spindle drive are presented. This demonstrates the effectiveness of the proposed control scheme as well as its suitability as an add-on strategy in existing positioning devices.
VI112-07
Nonlinear Adaptive Control Regular Session
Chair: Bobtsov, AlexeyITMO University
Co-Chair: Limon, DanielUniversidad De Sevilla
Paper VI112-07.1 
PDF · Video · Adaptive Full State Observer for Nonsalient PMSM with Noised Measurements of the Current and Voltage

Pyrkin, AntonITMO University
Vedyakov, AlexeyITMO University
Bobtsov, AlexeyITMO University
Bazylev, DmitryITMO University
Sinetova, MadinaITMO University
Ovcharov, AlexeyITMO University
Antipov, VladislavITMO University
Keywords: Adaptive observer design, Nonlinear system identification, Identification for control
Abstract: An algorithm of adaptive estimation of the magnetic flux for the non-salient permanent magnet synchronous motor (PMSM) for the case when measurable electrical signals are corrupted by a constant offset is presented. A new nonlinear parameterization of the electric drive model based on dynamical regressor extension and mixing (DREM) procedure is proposed. Due to this parameterization the problem of flux estimation is translated to the auxiliary task of identification of unknown constant parameters related to measurement errors. It is proved that when both current and voltage measurements are biased the proposed algorithm ensures convergence of the flux observation error to a bounded set. At the same time the position error converges to zero. The observer provides global exponential convergence if the corresponding regression function satisfies the persistent excitation condition. If the regression function is not square integrable the global asymptotic convergence is ensured. In comparison with known analogues this paper gives a constructive way of the flux reconstruction for a nonsalient PMSM with guaranteed performance (low oscillation, convergence rate regulation) and, from other hand, a straightforwardly easy implementation of the proposed observer to embedded systems.
Paper VI112-07.2 
PDF · Video · Iteration-Dependent High-Order Internal Model Based Iterative Learning Control for Discrete-Time Nonlinear Systems with Time-Iteration-Varying Parameter

Yu, MiaoZhejiang University
Chai, ShengZhejiang University
Keywords: Iterative and Repetitive learning control, Nonlinear adaptive control
Abstract: In this paper, an adaptive iterative learning control (AILC) scheme is designed for discrete-time nonlinear systems with random initial condition and time-iteration-varying parameter. The time-iteration-varying parameter is generated by a general iteration-varying high-order internal model (HOIM) with iteration-varying order and coefficients, and the parameter updating law is designed based on least square method. Compared with the existing works based on iteration-invariant HOIM with fixed order and coefficients, our work significantly extends the application scope of HOIM-based ILC. Using the designed HOIM based iterative learning controller, the learning convergence in the iteration domain is guaranteed through rigorous theoretical analysis under Lyapunov theory. Moreover, an illustrative example is given to demonstrate the effectiveness of the proposed method.
Paper VI112-07.3 
PDF · Video · Training Neural Networks for Plant Estimation, Control and Disturbance Rejection

Kotzé, HenryStellenbosch University
Kamper, HermanStellenbosch University
Jordaan, Hendrik WillemStellenbosch University
Keywords: Neural and fuzzy adaptive control, Adaptive observer design, Nonlinear adaptive control
Abstract: Neural networks are used in control systems to combat difficulties which nonlinear and linear controllers struggle to compensate for, such as environmental and model uncertainties. Neural networks have shown promising results as controllers or estimators of these uncertainties. However, few studies expand on important aspects on using and training a neural network, such as the dataset, input and output pairs, and the training of the different controllers and estimators. In this paper, a dataset used for neural controllers and estimators are presented which contains more complexity than that of the expected test environment. The training of different neural controllers and estimators are presented: estimators for the forward dynamics and disturbances, a feedback controller, a feedback linearisation controller and a disturbance rejection controller. For each neural component, the input and output pairs are presented with results of them performing in a test environment. From these results it was evident that through the use of the proposed dataset and training method the neural networks succeeded in fulfilling its role in the control architectures.
Paper VI112-07.4 
PDF · Video · ASPR Based Output Regulation with Adaptive PFC and Feedforward Input Via Kernel Method

Mizumoto, IkuroKumamoto Univ
Akaike, KotaKumamoto University
Keywords: Neural and fuzzy adaptive control, Model reference adaptive control, Nonlinear adaptive control
Abstract: An adaptive control system design problem based on the almost strictly positive real-ness (ASPR-ness) is dealt with. For ASPR systems, one can easily design a stable adaptive output feedback control system, however, in the case where the system is not ASPR, in order to guarantee the stability of the adaptive system, an parallel feedforward compensator (PFC), which makes the resulting augmented system ASPR, is introduced. In the proposed method, an adaptive PFC design scheme for making the resulting augmented system ASPR and an adaptive feedforward input design scheme for attaining the output tracking are proposed by applying the kernel method for uncertain non-ASPR linear systems. The effectiveness of the proposed method is confirmed through numerical simulations for a simple uncertain system.
Paper VI112-07.5 
PDF · Video · Tube-Based Internal Model Control of Minimum-Phase Input-Affine MIMO Systems under Input Constraints

Ben Jemaa, KarimUlm University, Robert Bosch GmbH
Reimann, SvenRobert Bosch GmbH
Kotman, PhilippRobert Bosch GmbH
Graichen, KnutFriedrich-Alexander-University Erlangen-Nuremberg
Keywords: Nonlinear adaptive control
Abstract: In this paper an optimal control approach based on a combination of inversion-based control and internal model control (IMC) is designed to keep the controlled states of a minimum-phase input-affne MIMO system within predefined tubes while respecting input constraints. This contribution extends recently presented results for the SISO case to nonlinear input-affne MIMO systems. The developed approach uses ideas developed in the design of inversion-based IMC controllers for setpoint tracking and extends them to 'tube tracking'. It shows the interesting result that for input-affine systems the control task of maintaining each controlled state within a tube while minimizing energy consumption and respecting input constraints can be expressed as a convex quadratic optimization problem. This concept allows to handle dynamic systems where the number of control inputs differs from the number of controlled outputs are not equal. The control approach is illustrated by three simulation examples.
Paper VI112-07.6 
PDF · Video · Adaptive Stabilization by Delay with Biased Measurements

Efimov, DenisInria
Aranovskiy, StanislavCentrale Supelec - IETR
Fridman, EmiliaTel-Aviv Univ
Sokolov, DmitryUniversité De Lorraine
Wang, JianHangzhou Dianzi University
Bobtsov, AlexeyITMO University
Keywords: Nonlinear adaptive control, Continuous time system estimation, Adaptive observer design
Abstract: The problem of output robust adaptive stabilization for a class of Lipschitz nonlinear systems is studied under assumption that the measurements are available with a constant bias. The state reconstruction is avoided by using delayed values of the output in the feedback and adaptation laws. The control and adaptation gains can be selected as a solution of the proposed linear matrix inequalities (LMIs). The efficiency of the presented approach is demonstrated on a nonlinear pendulum through simulations
Paper VI112-07.7 
PDF · Video · Real-Time Optimization of Periodic Systems: A Modifier-Adaptation Approach

Mirasierra, VictorUniversidad De Sevilla
Vergara-Dietrich, Jose DoloresUniversidade Tecnológica Federal Do Paraná
Limon, DanielUniversidad De Sevilla
Keywords: Nonlinear adaptive control, Iterative and Repetitive learning control
Abstract: Modifier-Adaptation methodologies have been widely used to overcome plant-model mismatch and control a system to its steady-state optimal setpoint. They use gradient information of the real plant to design modifiers that correct the model, so that the first order necessary conditions for optimality of the model-based problem converge to those of the optimal one. In this paper, we get rid of the hypothesis that the plant optimum needs to be an equilibrium point. Instead, we only require it to be a periodic trajectory. We show the behaviour of the proposed approach by means of a motivating example that highlights the necessity of this formulation in cases where the system changes periodically through time.
Paper VI112-07.8 
PDF · Video · Online Adaptive Critic Robust Control of Discrete-Time Nonlinear Systems with Unknown Dynamics

Fu, HaoChina University of Geosciences
Chen, XinChina University of Geosciences
Wu, MinChina University of Geosciences
Keywords: Nonlinear adaptive control, Model reference adaptive control, Learning for control
Abstract: This paper concerns the optimal model reference adaptive control problem for unknown discrete-time nonlinear systems. For such problem, it is challenging to improve online learning efficiency and guaranteeing robustness to the uncertainty. To this end, we develop an online adaptive critic robust control method. In this method, a critic network and a new supervised action network are constructed to not only improve the real-time learning efficiency, but also obtain the optimal control performance. By combining the designed compensation control term, robustness is further guaranteed by compensating the uncertainty. The comparative simulation study is conducted to show the superiority of our developed method.
Paper VI112-07.9 
PDF · Video · Fixed-Time Control for a Class of Unknown Nonlinear Affine Systems and Its Applications to a Lithography Machine

Luo, KehanUniversity of Electronic Science and Technology of China
Zou, JianxiaoSchool of Automation Engineering, University of Electronic Scien
Kong, LinghuanUniversity of Electronic Science and Technology of China
He, WeiUniversity of Science and Technology Beijing
Keywords: Nonlinear adaptive control, Neural and fuzzy adaptive control
Abstract: The fixed-time control problems of a class of unknown nonlinear affine systems subject to external disturbances, unknown input dead zone and output constraints are considered in this paper. The novel fixed-time adaptive neural networks state feedback controller is designed in this paper. In the control design, the log-type barrier Lyapunov function (BLF) is chosen to handle the system output constraints. Then, neural networks(NNs) are applied to compensate for the adverse impact of unknown input dead zone and approximate unknown system functions. The novel virtual controllers and novel online updating laws of neural network weights are proposed to ensure the fixed-time stability of closed-loop systems. All the signals in closed-loop system are proved to be uniformly bounded with Lyapunov stability theory. Finally, a lithography machine experiment is used to illustrate the effectiveness of the proposed method.
Paper VI112-07.10 
PDF · Video · On the Decay Rate for Degenerate Gradient Flows Subject to Persistent Excitation

Prandi, DarioUniversité Paris-Saclay, CentraleSupélec, CNRS
Chitour, YacineUniversit'e Paris-Sud, CNRS, Centralesupelec
Mason, PaoloL2S CentraleSupélec, CNRS
Keywords: Nonlinear adaptive control, Nonlinear system identification, Input and excitation design
Abstract: In this paper, we estimate the worst rate of exponential decay of a class of degenerate gradient flows issued from adaptive control theory. Under a persistent excitation assumption, we provide upper bounds for this rate of decay consistent with previously known lower bounds and analogous stability results for more general classes of persistently excited signals. The strategy of proof consists in relating the worst decay rate to optimal control questions and studying in details their solutions.
Paper VI112-07.11 
PDF · Video · On the Line-Search Gradient Methods for Stochastic Optimization

Dvinskikh, DarinaWIAS
Ogaltsov, AleksandrHigher School of Economics, Moscow / Antiplagiat Company
Gasnikov, AlexanderMoscow Institute of Physics and Technology
Dvurechensky, PavelWeierstrass Institute
Spokoiny, VladimirWIAS and HU Berlin
Keywords: Stochastic adaptive control, Nonlinear adaptive control, Machine learning
Abstract: We consider several line-search based gradient methods for stochastic optimization: a gradient and accelerated gradient methods for convex optimization and gradient method for non-convex optimization. The methods simultaneously adapt to the unknown Lipschitz constant of the gradient and variance of the stochastic approximation for the gradient. The focus of this paper is to numerically compare such methods with state-of-the-art adaptive methods which are based on a different idea of taking norm of the stochastic gradient to define the stepsize, e.g., AdaGrad and Adam.
VI113
Systems and Signals - Discrete Event and Hybrid Systems
VI113-01 Cyber-Security and Safety of Discrete-Event Systems   Invited Session, 10 papers
VI113-02 Distributed Event-Triggered Control of Multi-Agent Systems   Invited Session, 6 papers
VI113-03 Formal Methods for Hybrid Systems   Open Invited Session, 11 papers
VI113-04 Modelling, Analysis and Control of Hybrid and Switched Systems   Regular Session, 10 papers
VI113-06 Reachability Analysis, Verification , and Abstraction of Hybrid Systems   Regular Session, 6 papers
VI113-07 Stability and Stabilization of Hybrid Systems   Regular Session, 13 papers
VI113-08 Supervisory Control and Analysis of Discrete Event Systems   Regular Session, 16 papers
VI113-01
Cyber-Security and Safety of Discrete-Event Systems Invited Session
Chair: Cai, KaiOsaka City University
Co-Chair: Yin, XiangShanghai Jiao Tong University
Organizer: Yin, XiangShanghai Jiao Tong University
Organizer: Cai, KaiOsaka City University
Paper VI113-01.1 
PDF · Video · Incremental Improvements of Heuristic Policies for Average-Reward Markov Decision Processes (I)

Reveliotis, Spyros A.Georgia Institute of Technology
Ibrahim, MichaelUniversity of Cairo
Keywords: Discrete event modeling and simulation, Learning for control, Queueing systems and performance model                                       
Abstract: Within the realm of Discrete Event Systems (DES) theory, the problem of performance optimization for many applications can be modeled as an infinite-horizon, average-reward Markov Decision Process (MDP) with a finite state space. In principle, these MDPs can be solved by various well-developed methods like value iteration, policy iteration and linear programming. But in reality, the tractability of these methods in the context of the aforementioned applications is compromised by the explosive size of the underlying state spaces, a problem that is known as ``the curse of dimensionality''. Hence, the corresponding performance optimization problems are frequently addressed by heuristic control policies. The considered work uses results from (i) the sensitivity analysis of Markov reward processes and (ii) the ranking & selection theory in statistics in order to develop a methodology for assessing the optimality of isolated decisions in the context of any well-defined heuristic control policy for the aforementioned MDPs. It also determines an improved decision when the current one is found to be suboptimal. Hence, when embedded in an iterative scheme, this methodology can support the incremental enhancement of the original heuristic policy in a way that controls, both, the computational and also the representational complexity of the new policy. Finally, an additional important feature of the presented methodology is that it can be executed either in an ``off-line'' mode, using a simulation of the dynamics of the underlying DES, or in an ``on-line'' mode, based on the sample path that is defined by the real-time dynamics of the controlled system.
Paper VI113-01.2 
PDF · Video · Verification of Infinite-Step Opacity Using Labeled Petri Nets (I)

Lan, HaoSouthwest Jiaotong University
Tong, YinSouthwest Jiaotong University
Seatzu, CarlaUniv. of Cagliari
Keywords: Petri nets, Discrete event modeling and simulation, Supervisory control and automata
Abstract: Opacity is an important information secure property. A system is said to be infinite-step opaque if the intruder is never able to ascertain that the system is or has been in a secret state at some time, based on its observation of the system evolution. This work aims to verify infinite-step opacity of discrete event systems modeled with labeled Petri nets. Based on the notion of basis reachability graph, a new structure called basis two-way observer is proposed to check infinite-step opacity of a bounded system, which is shown to be more efficient than the standard method based on the reachability graph.
Paper VI113-01.3 
Supervisor Reduction by Hiding Events (I)

Malik, RobiThe University of Waikato
Keywords: Supervisory control and automata, Event-based control
Abstract: This paper proposes a method to improve supervisor reduction for discrete event systems by first reducing the number of events. Supervisor reduction is a method to reduce the number of states of an automatically computed supervisor or controller in order to make it more manageable. This paper proposes to complement the most popular supervisor reduction algorithm currently in use by first reducing the supervisor's event set. Experimental results show that this does not only reduce the communication between the supervisor and plant, but also produces a simpler state machine that can be minimised more effectively.
Paper VI113-01.4 
PDF · Video · Confidentiality of Cyber-Physical Systems Using Event-Based Cryptography (I)

Lima, Públio M.Universidade Federal Do Rio De Janeiro
Carvalho, Lilian KawakamiUniversidade Federal Do Rio De Janeiro
Moreira, Marcos VicenteUniv. Fed. Rio De Janeiro
Keywords: Secure networked control systems, Discrete event modeling and simulation
Abstract: One of the most important challenges for the application of cyber-physical systems (CPS) in smart industries is ensuring its security against cyber attacks. In this paper, we consider that the CPS is abstracted as a Discrete-Event System (DES), and we consider cyber attacks where the intruder eavesdrops the sensor communication channel to detect the occurrence of a sequence in the secret behavior of the system. In order to prevent the attacker from getting information from the sensor channel, we introduce a new cryptographic scheme based on events called event-based cryptography. We also define the property of confidentiality of DES, present a necessary and sufficient condition for ensuring this property, and propose a verification test.
Paper VI113-01.5 
PDF · Video · Optimal Stabilization of Discrete Event Systems with Guaranteed Worst Cost (I)

Ji, YidingBoston University
Yin, XiangShanghai Jiao Tong University
Keywords: Supervisory control and automata, Stochastic control and game theory, Reachability analysis, verification and abstraction of hybrid systems
Abstract: This work investigates optimal stabilization with guaranteed worst-case performance of stochastic discrete event systems by supervisory control. We formulate the problem on probabilistic weighted automata. The system is driven to a specified set of target states after a finite number of transitions, thus stabilized. The cost of stabilization is concerned with the accumulative weight of transitions reaching target states. Our goal is to optimize the expected cost of reaching target states, while ensuring that the worst-case individual cost is bounded by a given threshold. Then we transform the supervisory control problem to a two-player stochastic game between the supervisor and the environment, which properly encodes the worst-case requirement. Finally an algorithm is presented to synthesize the optimal supervisor by leveraging results from Markov Decision Processes, which turns out to provably solve the original problem.
Paper VI113-01.6 
PDF · Video · Discrete Control of Response for Cybersecurity in Industrial Control (I)

Delaval, GwenaëlUniversité Grenoble Alpes
Hore, AyanInria Grenoble
Mocanu, StephaneGIPSA-Lab, Grenoble-INP
Muller, LucieInria Grenoble
Rutten, EricINRIA Rhône Alpes
Keywords: Discrete event modeling and simulation, Supervisory control and automata
Abstract: Cybersecurity in Industrial Control Systems (ICS) is a crucial problem, as recent history has shown. A notable characteristic of ICS, compared to Information Technology, is the necessity to take into account the physical process, and its specific dynamics and effects on the environment, when considering cybersecurity issues. Intrusion Detection Systems have been studied extensively. In our work, we address the less classic topic of response mechanisms, and their automation in a self-protection feedback loop. More precisely, we address self-protection seen as resilience, where the functionality of the system is maintained under attacks, be it in a degraded mode. We model this as a Discrete Event Systems supervisory control problem, involving a model of the plant’s possible behaviors, a model of considered attacks, and a formulation of the control objectives. We consider a case study, and perform a prototype implementation and simulation, using the Heptagon/BZR programming language and compiler/code generator, and targeting a multi-PLC experimental platform.
Paper VI113-01.7 
PDF · Video · Efficient Failure-Recovering Supervisors (I)

Paape, NickEindhoven University of Technology
van de Mortel-Fronczak, JoannaEindhoven University of Technology
Swartjes, LennartVanderlande Industries
Reniers, MichelTU/e
Keywords: Supervisory control and automata
Abstract: Automated systems require controllers which guarantee machine safety and specified functionality even in case of occurring defects. In literature, several methods can be found for formally deriving a supervisor providing such guarantees, including the existence of failure recovery. In this paper, an extension is proposed so that the derived supervisor not only guarantees the existence of failure recovery, but also enforces a shortest path for it. To this end, a two-step procedure is defined for supervisor derivation, in which two algorithms are involved.
Paper VI113-01.8 
PDF · Video · Opacity Enforcing Supervisory Control Using Non-Deterministic Supervisors (I)

Xie, YifanShanghai Jiao Tong University
Yin, XiangShanghai Jiao Tong University
Li, ShaoyuanShanghai Jiao Tong Univ
Keywords: Supervisory control and automata, Discrete event modeling and simulation, Diagnosis of discrete event and hybrid systems
Abstract: In this paper, we investigate the enforcement of opacity via supervisory control in the context of discrete-event systems. A system is said to be opaque if the intruder, which is modeled as a passive observer, can never infer confidentially that the system is at a secret state. The design objective is to synthesize a supervisor such that the closed-loop system is opaque even though the control policy is publicly known. In this paper, we propose to use non-deterministic supervisors to enforce opacity. A non-deterministic supervisor provides a set of control decisions at each instant, and randomly picks a specific control decision from the decision set. Such a non-deterministic control mechanism can enhance the plausible deniability of the controlled system as the online control decision cannot be implicitly inferred from the control policy. We provide an effective approach to synthesize a non-deterministic opacity-enforcing supervisor. Furthermore, we show that non-deterministic supervisors are strictly more powerful than deterministic supervisors in the sense that there may exist a non-deterministic opacity-enforcing supervisor even when deterministic supervisors cannot enforce opacity.
Paper VI113-01.9 
PDF · Video · Maximally Permissive Supervisor Control of Timed Discrete-Event Systems under Partial Observation (I)

Yang, ZitengShanghai Jiao Tong University
Yin, XiangShanghai Jiao Tong University
Li, ShaoyuanShanghai Jiao Tong Univ
Keywords: Supervisory control and automata, Discrete event modeling and simulation, Diagnosis of discrete event and hybrid systems
Abstract: In this paper, we investigate the supervisory control problem for timed discrete-event systems (TDES) under partial observation. In the timed setting, the system consists of both standard logical events and time event, where the former can be disabled directly by the supervisor if it is controllable while the latter can only be preempted by forcing the occurrences of forcible events. We consider a general control mechanism where the supervisor can choose which events to force dynamically online at each instant. The design objective is to synthesize a maximally-permissive supervisor to restrict the behavior of the system such that the closed-loop language is within a safe specification language. Effective procedure is presented to synthesize such a supervisor. To our knowledge, how to synthesize a maximally-permissive partial-observation supervisor for has not been solved for timed DES. We provide a solution to this problem under a general control mechanism.
Paper VI113-01.10 
PDF · Video · Towards Probabilistic Intrusion Detection in Supervisory Control of Discrete Event Systems (I)

Meira-Góes, RômuloUniversity of Michigan
Keroglou, ChristoforosUniversity of Michigan, Ann Arbor
Lafortune, StephaneUniv. of Michigan
Keywords: Supervisory control and automata, Discrete event modeling and simulation
Abstract: In control systems, sensor deception is a class of attacks where an attacker manipulates sensor readings to cause damage to the system. Our work investigates quantitative measurements to detect this class of attacks in the context of stochastic supervisory control. We introduce the notion of e-safe systems, which is a first step to generalize qualitative intrusion detection conditions to quantitative intrusion detection conditions. We provide sufficient and necessary conditions to verify if a system is e-safe. Moreover, we provide an algorithm that verifies these conditions, which implies that the problem is decidable.
VI113-02
Distributed Event-Triggered Control of Multi-Agent Systems Invited Session
Chair: He, WangliEast China University of Science and Technology
Co-Chair: Xu, WenyingSoutheast University
Organizer: He, WangliEast China University of Science and Technology
Organizer: Tang, YangEast China University of Science and Technology
Organizer: Han, Qing-LongSwinburne University of Technology
Paper VI113-02.1 
PDF · Video · Event-Triggered Finite-Time Consensus under Directed Graphs (I)

Jin, XinEast China University of Science and Technology
Zhang, WenbingYangzhou University
Wu, XiaotaiAnhui Polytechnic University
Tang, YangEast China University of Science and Technology
Keywords: Multi-agent systems, Consensus, Event-based control
Abstract: This paper focuses on deal with the nite-time consensus with event-triggered control strategy for multi-agent systems (MASs). An event-triggered protocol for nite-time consensus is designed using relative measurements. The coordination measurement error is utilized in the triggering condition design for the purpose of removing the prerequisite of topology graph knowledge. Under strongly connected graph assumptions, by utilizing the proposed consensus protocol, all agents can complete consensus and Zeno behaviour will not happen in a settling time. Next, by decomposing the Laplacian matrix in Frobenius norm form, the results are extended to the more general graphs containing a directed spanning tree. At last, a numerical example demonstrates the validity of the algorithm results.
Paper VI113-02.2 
PDF · Video · Dynamic Adaptive Event-Triggered Scheme for General Linear Multi-Agent Systems (I)

Xu, WenyingSoutheast University, Nanjing
Ho, Daniel W. C.City Univ. of Hong Kong
He, WangliEast China University of Science and Technology
Keywords: Consensus, Control under communication constraints, Distributed control and estimation
Abstract: This paper proposes a novel dynamic adaptive event-triggered scheme to deal with consensus problems in a class of general linear multi-agent systems. Firstly, a fully distributed event-triggered consensus protocol is proposed by assigning a time-varying coupling weight for each edge. Then by introducing an additional internal dynamic variable, a dynamic adaptive event condition is skillfully constructed and Zeno behavior is also successfully excluded. Based on this, the asymptotic consensus is eventually achieved and the frequency of communication among agents is significantly reduced. Finally, one simulation example is provided to verify the effectiveness of the proposed scheme.
Paper VI113-02.3 
PDF · Video · Co-Design of Sampling Pattern and Control in Self-Triggered Model Predictive Control for Sampled-Data Systems (I)

Cui, DiNorthwestern Polytechnical University
Li, HuipingNorthwestern Polytechnical University
Keywords: Event-based control, Model predictive control of hybrid systems, Control over networks
Abstract: This paper studies the event-triggered model predictive control (MPC) problem for networked control systems with input constraints, where the control is of the sampled data form. A novel self-triggered MPC (STMPC) method which enables the optimal design of sampling pattern and control law is proposed to reduce the conservatism of separate design of trigger and control law in existing approaches. The conditions on ensuring algorithm feasibility and closed-loop system stability are developed. In addition, an upper bound of the closed-loop system performance is derived which provides performance guarantee for the designed STMPC. Finally, simulation results are presented to verify the effectiveness of the proposed STMPC method.
Paper VI113-02.4 
PDF · Video · Risk Assessment of Multi-Area Interconnected Power System under Gas Station Network Attacked (I)

Li, XueShanghai University
Zhang, ZhourongShanghai Key Laboratory of Power Station Automation Technology,
Du, DajunQueen's University Belfast
Dong, JingShanghai Key Laboratory of Power Station Automation Technology,
Wang, Yu-LongShanghai University
Keywords: Secure networked control systems, Control over networks
Abstract: This paper mainly investigates the risk assessment of multi-area interconnected power system with probabilistic optimal power flow (POPF) for charging load of plug-in hybrid electric vehicle (PHEV) under gas station network attacked. Firstly, the PHEV charging model is developed by analyzing the change of PHEV operation mode after running out of gasoline. Secondly, in a multi-region interconnected power system, a line overload risk index is established to evaluate the impact of PHEV charging on the tie-line powers with the gas station network unavailable, and POPF considering PHEVs, wind and photovoltaic generation is employed to reduce the risk of system operation. Finally, the method is tested on IEEE 118-bus system to analyze the impacts of PHEV charging on tie-line powers and the entire system under gas station network attacked, and the economy and safety of system operation are evaluated before and after optimization.
Paper VI113-02.5 
PDF · Video · Resilient Distributed Event-Triggered Control of Vehicle Platooning under DoS Attacks (I)

Xiao, ShunyuanNanjing University of Science and Technology
Ge, XiaohuaSwinburne University of Technology
Han, Qing-LongSwinburne University of Technology
Cao, ZhenweiSwinburne University
Zhang, YijunNanjing University of Science and Technology
Wang, HonghaiCollege of Information Science and Engineering, NortheasternUniv
Keywords: Secure networked control systems, Control under communication constraints, Networked embedded control systems
Abstract: This paper is concerned with resilient distributed event-triggered control of a platoon of automatic vehicles under DoS attacks. First, an event-triggered transmission mechanism resilient to energy-limited DoS attacks is proposed to save the possible communication resources within inter-vehicle communication channels. A consensus-based distributed control strategy, which acts on each vehicle in the platoon, is developed to account for the information cooperation of leading and following vehicles under the proposed resilient event-triggered data transmission mechanism. Second, an attack-tolerant performance index with certain resilience level is put forward in such a way to achieve resilience evaluation of the vehicular platoon system. Third, sufficient conditions are derived for ensuring the asymptotic stability of the resulting vehicular platoon system while preserving the prescribed resilience performance requirement. Furthermore, a co-design approach to the distributed platoon controller as well as the resilient event triggering condition is presented. Finally, a case study under a predecessor-leader following topology is given to demonstrate the effectiveness of the obtained results.
Paper VI113-02.6 
PDF · Video · Quantized Consensus of Linear Multi-Agent Systems under an Event-Triggered Strategy (I)

Luo, TinghuiEast China University of Science and Technology
He, WangliEast China University of Science and Technology
Xu, WenyingCity University of Hong Kong
Keywords: Consensus, Event-based control, Quantized systems
Abstract: This paper addresses a quantized consensus problem of general linear multi-agent systems in a symmetric network under an event-triggered scheme. Firstly, a distributed event-triggered strategy is developed with a dynamic threshold to reduce the unnecessary control update. Then, based on absolute quantized state measurements, a distributed controller is proposed and then a consensus criterion is derived, which ensures bounded consensus of linear multi-agent systems. The Zeno behavior is also successfully excluded. Finally, a numerical simulation is presented to validate theoretical results.
VI113-03
Formal Methods for Hybrid Systems Open Invited Session
Chair: Sanfelice, RicardoUniversity of California Santa Cruz
Co-Chair: Prabhakar, PavithraKansas State University
Organizer: Liu, JunUniversity of Waterloo
Organizer: Prabhakar, PavithraKansas State University
Organizer: Sanfelice, RicardoUniversity of California Santa Cruz
Paper VI113-03.1 
PDF · Video · Abstraction of Monotone Systems Based on Feedback Controllers (I)

Sinyakov, VladimirCNRS
Girard, AntoineCNRS
Keywords: Reachability analysis, verification and abstraction of hybrid systems
Abstract: In this paper, we consider the problem of computation of efficient symbolic abstractions for a certain subclass of continuous-time monotone control systems. The new abstraction algorithm utilizes the properties of such systems to build symbolic models with the same number of states but fewer transitions in comparison to the one produced by the standard algorithm. At the same time, the new abstract system is at least as controllable as the standard one. The proposed algorithm is based on the solution of a region-to-region control synthesis problem. This solution is formally obtained using the theory of viscosity solutions of the dynamic programming equation and the theory of differential equations with discontinuous right-hand side. In the new abstraction algorithm, the symbolic controls are essentially the feedback controllers that solve this control synthesis problem. The improvement in the number of transitions is achieved by reducing the number of successors for each symbolic control. The approach is illustrated by an example that compares the two abstraction algorithms.
Paper VI113-03.2 
PDF · Video · Interval Reachability Analysis Using Second-Order Sensitivity (I)

Meyer, Pierre-JeanUniversity of California, Berkeley
Arcak, MuratUC Berkeley
Keywords: Reachability analysis, verification and abstraction of hybrid systems
Abstract: We propose a new approach to compute an interval over-approximation of the finite time reachable set for a large class of nonlinear systems. This approach relies on the notions of sensitivity matrices, which are the partial derivatives representing the variations of the system trajectories in response to variations of the initial states. Using interval arithmetics, we first over-approximate the possible values of the second-order sensitivity at the final time of the reachability problem. Then we exploit these bounds and the evaluation of the first-order sensitivity matrices at a few sampled initial states to obtain an over-approximation of the first-order sensitivity, which is in turn used to over-approximate the reachable set of the initial system. Unlike existing methods relying only on the first-order sensitivity matrix, this new approach provides guaranteed over-approximations of the first-order sensitivity and can also provide such over-approximations with an arbitrary precision by increasing the number of samples.
Paper VI113-03.3 
PDF · Video · Continuous and Discrete Abstractions for Planning, Applied to Ship Docking (I)

Meyer, Pierre-JeanUniversity of California, Berkeley
Yin, HeUniversity of California, Berkeley
Brodtkorb, Astrid HNorwegian University of Science and Technology
Arcak, MuratUC Berkeley
Soerensen, AsgeirNorwegian University of Science and Technology
Keywords: Reachability analysis, verification and abstraction of hybrid systems
Abstract: We propose a hierarchical control framework for the synthesis of correct-by-construction controllers for nonlinear control-affine systems with respect to reach-avoid-stay specifications. We first create a low-dimensional continuous abstraction of the system and use Sum-of-Squares (SOS) programming to obtain a low-level controller ensuring a bounded error between the two models. We then create a discrete abstraction of the continuous abstraction and use formal methods to synthesize a controller satisfying the specifications shrunk by the obtained error bound. Combining both controllers finally solves the main control problem on the initial system. This two-step framework allows the discrete abstraction methods to deal with higher-dimensional systems which may be computationally expensive without the prior continuous abstraction. The main novelty of the proposed SOS continuous abstraction is that it allows the error between abstract and concrete models to explicitly depend on the control input of the abstract model, which offers more freedom in the choice of the continuous abstraction model and provides lower error bounds than when only the states of both models are considered. This approach is illustrated on the docking problem of a marine vessel.
Paper VI113-03.4 
PDF · Video · Lazy Safety Controller Synthesis with Multi-Scale Adaptive-Sampling Abstractions of Nonlinear Systems (I)

Ivanova, ElenaCNRS, CentraleSupelec, Université Paris-Sud, Université Paris-Sa
Girard, AntoineCNRS
Keywords: Reachability analysis, verification and abstraction of hybrid systems
Abstract: In this paper, we present an abstraction-based approach to safety controller synthesis for continuous-time nonlinear systems. To reduce the computational burden associated with symbolic control approaches, we develop a lazy controller synthesis algorithm, which uses the incremental forward exploration of the symbolic dynamics, allowing us to restrict the controller synthesis computations to reachable states only. We propose using this algorithm with novel multi-scale abstractions, which also use adaptive time sampling. Transition duration is constrained by intervals that must contain the reachable set, which enables better control of the symbolic transitions as opposed to using transitions of predetermined duration. Implementation of the algorithm and controller refinement are discussed. We provide a simple example to illustrate these benefits of the approach.
Paper VI113-03.5 
PDF · Video · On Symbolic Control Design of Nonlinear Systems with Dynamic Regular Language Specifications (I)

Masciulli, TommasoUniversity of L'Aquila
Pola, GiordanoUniversity of L'Aquila
Keywords: Quantized systems, Supervisory control and automata
Abstract: Formal methods are becoming rather popular in the research community working on hybrid systems because they provide a systematic approach to design complex and heterogeneous systems of interest in e.g. industrial world. In this paper we consider a control problem where the plant is a nonlinear system, the controller is a finite state machine, easily implementable in digital devices, and the specification is a regular language and, it is dynamic. The motivation for considering dynamic specifications comes from some relevant and concrete applications where environment, external to the plant, may change in time and therefore designed controllers need to timely reconfigure to properly deal with new scenario. We propose an approach to reduce on–line computations for controller reconfiguration which exhibits gain in terms of time computational complexity. The results we present are based on the use of symbolic models and on regular language theory.
Paper VI113-03.6 
PDF · Video · A Necessary Condition on Chain Reachable Robustness of Dynamical Systems (I)

Fitzsimmons, MaxwellUniversity of Waterloo
Liu, JunUniversity of Waterloo
Keywords: Reachability analysis, verification and abstraction of hybrid systems
Abstract: It is ``folklore'' that the solution to a set reachability problem for a dynamical system is only noncomputable because of non-robustness reasons. A robustness condition that can be imposed on a dynamical system is the requirement of the chain reachable set to equal the closure of the reachable set. We claim that this condition necessarily imposes strong conditions on the dynamical system. For instance, if the space is connected and compact and we are computing a chain reachable robust single valued function f then f cannot have an unstable fixed point or unstable periodic cycle.
Paper VI113-03.7 
PDF · Video · Compositional Construction of Control Barrier Functions for Networks of Continuous-Time Stochastic Systems (I)

Nejati, AmenehTechnical University of Munich (TUM)
Soudjani, SadeghNewcastle University
Zamani, MajidUniversity of Colorado Boulder
Keywords: Control of networks, Synthesis of stochastic systems, Stochastic hybrid systems
Abstract: In this paper, we propose a compositional framework for the construction of control barrier functions for networks of continuous-time stochastic control systems. The proposed scheme is based on a notion of so-called pseudo-barrier functions computed for subsystems, using which one can synthesize state-feedback controllers for interconnected systems enforcing safety specifications over a finite-time horizon. Particularly, we first leverage sufficient small-gain type conditions to compositionally construct control barrier functions for interconnected systems based on the corresponding pseudo-barrier functions computed for subsystems. Then, using the constructed control barrier functions, we quantify upper bounds on exit probabilities - the probability that an interconnected system reaches certain unsafe regions - in a finite-time horizon. We employ a systematic technique based on the sum-of-squares optimization program to search for pseudo-barrier functions of subsystems while synthesizing safety controllers. We demonstrate our proposed results by applying them to a temperature regulation in a network of 1000 rooms.
Paper VI113-03.8 
PDF · Video · Compositional Construction of Control Barrier Certificates for Large-Scale Interconnected Stochastic Systems (I)

Anand, MahathiLudwig Maximilian University of Munich
Lavaei, AbolfazlLudwig Maximilian University of Munich (LMU)
Zamani, MajidUniversity of Colorado Boulder
Keywords: Synthesis of stochastic systems, Control of networks, Stochastic hybrid systems
Abstract: This paper proposes a compositional approach for constructing control barrier certificates of large-scale interconnected discrete-time stochastic control systems. The proposed compositional methodology is based on a notion of control sub-barrier certificates enabling one to construct control barrier certificates of interconnected systems by leveraging some small-gain type conditions. The main goal is to synthesize control policies satisfying safety properties for interconnected systems utilizing those control sub-barrier certificates of subsystems while providing upper bounds on the probability that interconnected systems reach unsafe regions in finite-time horizons. A sum-of-squares optimization problem is formulated for searching control sub-barrier certificates and corresponding local control policies satisfying safety specifications. The proposed compositional approaches are illustrated on a temperature regulation in a circular building containing 1000 rooms by compositionally synthesizing safety controllers to maintain the temperature of each room in a comfort zone in a bounded-time horizon.
Paper VI113-03.9 
PDF · Video · Compositional Synthesis of Symbolic Models for Infinite Networks (I)

Swikir, AbdallaTechnical University of Munich
Noroozi, NavidOtto Von Guericke University Magdeburg
Zamani, MajidUniversity of Colorado Boulder
Keywords: Quantized systems, Control of networks, Multi-agent systems
Abstract: In this paper, we provide a compositional method for the construction of symbolic models (a.k.a. finite abstractions) for infinite networks of discrete-time control systems. The concrete infinite network and its symbolic model are related by a so-called alternating simulation function which allows one to quantify the mismatch between the output behavior of the infinite interconnection of concrete subsystems and that of their symbolic models. We show that such an alternating simulation function can be obtained compositionally by assuming some small-gain type conditions and composing so-called local alternating simulation functions constructed for subsystems. Assuming certain stability property of concrete subsystems, we also provide a technique to synthesize their symbolic models together with their corresponding local alternating simulation functions. Finally, we apply our results to a traffic network divided into infinitely many cells.
Paper VI113-03.10 
PDF · Video · Optimization-Based Motion Planning and Runtime Monitoring for Robotic Agent with Space and Time Tolerances (I)

Lin, ZhenyuUniversity of Maryland, College Park
Baras, John S.Univ. of Maryland
Keywords: Optimal control of hybrid systems, Event-based control, Model predictive control of hybrid systems
Abstract: We present an optimization-based approach for robot planning, monitoring and self-correction problems under signal temporal logic speci fications (STL). The STL speci fications are translated into mixed-integer linear constraints, and we generate the reference trajectory by solving a mixed-integer-linear-programming (MILP) to maximize the overall space and time tolerances. During runtime execution, a prediction module is constantly evaluating the robustness degree of the predicted trajectory, and a self-correction module based on event- triggered model predictive control (MPC) has been designed to predict and correct possible future violations of the specifi cations. Simulation results show that with our approach, the robotic agent is able to generate a path that satisfi es the STL speci fications while maximizing space and time tolerances, and able to make corrections when there are possible violations of the specifi cations during runtime execution.
Paper VI113-03.11 
PDF · Video · Reachability-Based Human-In-The-Loop Control with Uncertain Specifications

Gao, YulongThe Royal Institute of Technology (KTH)
Jiang, Frank J.KTH Royal Institute of Technology
Ren, XiaoqiangShanghai University
Xie, LihuaNanyang Technological University
Johansson, Karl H.Royal Institute of Technology
Keywords: Networked robotic systems, Networked embedded control systems, Control over networks
Abstract: We propose a shared autonomy approach for implementing human operator decisions onto an automated system during multi-objective missions, while guaranteeing safety and mission completion. A mission is specified as a set of linear temporal logic (LTL) formulae. Then, using a novel correspondence between LTL and reachability analysis, we synthesize a set of controllers for assisting the human operator to complete the mission, while guaranteeing that the system maintains specified spatial and temporal properties. We assume the human operator's exact preference of how to complete the mission is unknown. Instead, we use a data-driven approach to infer and update the automated system's internal belief of which specified objective the human intends to complete. If, while the human is operating the system, she provides inputs that violate any of the invariances prescribed by the LTL formula, our verified controller will use its internal belief of the human operator's intended objective to guide the operator back on track. Moreover, we show that as long as the specifications are initially feasible, our controller will stay feasible and can guide the human to complete the mission despite some unexpected human errors. We illustrate our approach with a simple, but practical, experimental setup where a remote operator is parking a vehicle in a parking lot with multiple parking options. In these experiments, we show that our approach is able to infer the human operator's preference over parking spots in real-time and guarantee that the human will park in the spot safely.
VI113-04
Modelling, Analysis and Control of Hybrid and Switched Systems Regular Session
Chair: Sanfelice, RicardoUniversity of California Santa Cruz
Co-Chair: Pepe, PierdomenicoUniversity of L'Aquila
Paper VI113-04.1 
PDF · Video · Index-2 Hybrid DAE: A Case Study with Well-Posedness and Numerical Analysis

Rocca, AlexandreInria Grenoble, Uga, Ljk
Acary, VincentINRIA Grenoble
Brogliato, BernardUR Rhone-Alpes
Keywords: Hybrid and switched systems modeling
Abstract: In this work, we study differential algebraic equations with constraints defined in a piecewise manner using a conditional statement. Such models classically appear in systems where constraints can evolve in a very small time frame compared to the observed time scale. The use of conditional statements or hybrid automata is a powerful way to describe such systems and are, in general, well suited to simulation with event driven numerical schemes. However, such methods are often subject to chattering at mode switch in presence of sliding modes, or can result in Zeno behaviours. In contrast, the representation of such systems using differential inclusions and method from non-smooth dynamics are often closer to the physical theory but may be harder to interpret. Associated time-stepping numerical methods have been extensively used in mechanical modelling with success and then extended to other fields such as electronics and system biology. In a similar manner to the previous application of non-smooth methods to the simulation of piecewise linear ODEs, non-smooth event-capturing numerical schemes are applied to piecewise linear DAEs. In particular, the detailed study of a 2-D dynamical system of index-2 with a switching constraint using set-valued operators, is presented.
Paper VI113-04.2 
PDF · Video · Enhancing Low-Rank Solutions in Semidefinite Relaxations of Boolean Quadratic Problems

Cerone, VitoPolitecnico Di Torino
Fosson, Sophie MariePolitecnico Di Torino
Regruto, DiegoPolitecnico Di Torino
Keywords: Hybrid and switched systems modeling
Abstract: Boolean quadratic optimization problems occur in a number of applications. Their mixed integer-continuous nature is challenging, since it is inherently NP-hard. For this motivation, semidefinite programming relaxations (SDR’s) are proposed in the literature to approximate the solution, which recasts the problem into convex optimization. Nevertheless, SDR’s do not guarantee the extraction of the correct binary minimizer. In this paper, we present a novel approach to enhance the binary solution recovery. The key of the proposed method is the exploitation of known information on the eigenvalues of the desired solution. As the proposed approach yields a non-convex program, we develop and analyze an iterative descent strategy, whose practical effectiveness is shown via numerical results.
Paper VI113-04.3 
PDF · Video · ISS Small-Gain Theorem for Networked Discrete-Time Switching Systems

Pepe, PierdomenicoUniversity of L'Aquila
Keywords: Hybrid and switched systems modeling, Control of networks, Stability and stabilization of hybrid systems
Abstract: In this paper it is proved that a discrete-time switching system, equipped with a given switches digraph, is input-to-state stable, provided that there exist multiple Lyapunov functions (one for each mode) for each subsystem in the network, satisfying suitable standard inequalities, and provided that a set of suitable vector small-gain conditions are satisfied. The small-gain theorem here provided for the input-to-state stability takes into account the switches digraph. That is, the less is the number of edges in the switches digraph, the less is the number of involved Lyapunov inequalities and small-gain conditions which, if satisfied, guarantee the input-to-state stability of the entire switching system under study. The multiple Lyapunov functions for the entire system, guaranteeing the input-to-state stability, are determined by the multiple Lyapunov functions for each subsystem in the family. To the author's best knowledge, this is the first paper in the literature concerning small-gain theorems for the input-to-state stability of nonlinear discrete-time switching systems with given switches digraphs.
Paper VI113-04.4 
PDF · Video · An Adaptive Hybrid Control Algorithm for Sender-Receiver Clock Synchronization

Guarro, MarcelloUniversity of California, Santa Cruz
Sanfelice, RicardoUniversity of California Santa Cruz
Keywords: Hybrid and switched systems modeling, Control over networks, Sensor networks
Abstract: This paper presents an innovative hybrid systems approach to the sender-receiver synchronization of timers. Via the hybrid systems framework, we unite the traditional sender-receiver algorithm for clock synchronization with an, online, adaptive strategy to achieve synchronization of the clock rates to exponentially synchronize a pair of clocks connected over a network. Following the conventions of the algorithm, clock measurements of the nodes are given at periodic time instants, and each node uses these measurements to achieve synchronization. For this purpose, we introduce a hybrid system model of a network with continuous and impulsive dynamics that captures the sender-receiver algorithm as a state-feedback controller to synchronize the network clocks. Moreover, we provide sufficient design conditions that ensure attractivity of the synchronization set.
Paper VI113-04.5 
PDF · Video · An Algebraic Approach for Discrete Dynamic Reconstruction for Switched Bilinear Systems

Motchon, Koffi M. DjidulaUniversité De Reims Champagne Ardenne, CReSTIC EA 3804
Rajaoarisoa, LalaInstitut Mines Télecom. Mines De Douai
Pekpe, Komi MidzodziUniversity Lille 1
Etienne, LucienIMT Lille-Douai
Lecoeuche, StéphaneIMT Lille Douai
Keywords: Hybrid and switched systems modeling, Nonlinear system identification, Continuous time system estimation
Abstract: Estimation of the switching signal of continuous-time switched bilinear systems from input-output measurements is addressed in this paper. First, the uniqueness problem of the switching signal reconstruction from the input-output data is studied in terms of distinguishability analysis of the operating modes. In this context, a numerically verifiable condition for the operating modes distinguishability is established. This condition also provides a characterization of regularly persistent control inputs ensuring the unique determination of the switching signal. For these class of control inputs, an algorithm is then provided for the estimation problem. The proposed approach is based on a compatibility test between the input-output measurements and the dynamical behaviour of the modes.
Paper VI113-04.6 
PDF · Video · Adaptive Cruise Control with Timed Automata

Kara, Mustafa YavuzMiddle East Technical University
Aydin Gol, EbruMiddle East Technical University
Keywords: Hybrid and switched systems modeling, Reachability analysis, verification and abstraction of hybrid systems, Supervisory control and automata
Abstract: An adaptive cruise control (ACC) system maintains the vehicle at the given target speed when there is no leading vehicle in the sensor range. On the other hand, in the presence of a leading vehicle, the system maintains a safe distance between the vehicles while driving as close as possible to the target speed. For such an automated system, besides meeting safety requirements, it is also important to provide a comfortable drive. In this paper, we develop a formal model for adaptive cruise control system based on timed automata and express specifications in temporal logics. The proposed model supports different acceleration levels. Parametric constraints govern the transitions to the states associated with acceleration levels. The proposed parameter optimization methods generate parameter valuations for particular driving styles while guaranteeing safety and the specifications over the target speed. Therefore, the resulting system is guaranteed to satisfy the requirements while the driver comfort is optimized. The models and the synthesis approach are illustrated with examples.
Paper VI113-04.7 
PDF · Video · Indirect Model Reference Adaptive Control of Piecewise Affine Systems with Concurrent Learning

Liu, TongTechnische Universität München
Buss, MartinTechnische Universitaet Muenchen
Keywords: Hybrid and switched systems modeling, Stability and stabilization of hybrid systems, Adaptive gain scheduling autotuning control and switching control
Abstract: In this paper, we propose a concurrent learning-based indirect model reference adaptive control approach for multivariable piecewise affine systems as an enhancement of our previous work. The main advantage of the concurrent learning-based approach is that the linear independence condition of the recorded data suffices for the convergence of the estimated system parameters. The classical persistent excitation assumption of the input signal is not required. Moreover, it is proved that the closed-loop system is stable even when the system enters the sliding mode. The numerical example shows that the concurrent learning-based approach exhibits better tracking performance and achieves parameter convergence when compared with our previously proposed approach.
Paper VI113-04.8 
PDF · Video · New Results on Stabilization of Stochastic Switching Systems Subject to Partly Available Semi-Markov Kernel

Ning, ZepengHarbin Institute of Technology
Cai, BoHarbin Institute of Technology
Zhang, RuixianHarbin Institute of Technology
Zhang, LixianHarbin Institute of Technology
Keywords: Hybrid and switched systems modeling, Synthesis of stochastic systems, Stochastic hybrid systems
Abstract: This paper investigates the stabilization issue for a class of discrete-time stochastic switching systems. The switching behavior is dominated by a semi-Markov process with finite sojourn time. Allowing for the fact that it is often difficult to get complete semi-Markov kernel (SMK) in practice, the elements in SMK of the model under study are considered to be partly accessible, which is more general than both semi-Markov model with complete SMK and Markov model with unknown transition probabilities. Sufficient stability condition is derived for the underlying system without any a priori knowledge, based on which a stabilization criterion is presented such that the closed-loop stochastic switching systems can be mean-square stable. In the end, the validity of the theoretical results is testified by a numerical example.
Paper VI113-04.9 
PDF · Video · Structural Controllability of Switching Max-Plus Linear Systems

Gupta, AbhimanyuDelft University of Technology
van den Boom, Ton J. J.Delft Univ. of Tech
van der Woude, JacobDelft University of Technology
De Schutter, BartDelft University of Technology
Keywords: Max-plus algebra, Stability and stabilization of hybrid systems, Hybrid and switched systems modeling
Abstract: We introduce a framework for studying controllability properties of discrete-event systems modelled as switching max-plus linear systems. In this framework, we generalise the notion of structural controllability to include the switching phenomenon. Such models provide an additional discrete input to change the synchronisation and/or ordering constraints of the system. In this paper, we solve the problem of assigning the throughput of the system by suitable controller configurations. In particular, we formulate structural conditions for the existence of controllers achieving stable stationary behaviour. We also classify the achievable throughput under different controller configurations.
Paper VI113-04.10 
PDF · Video · Modeling DC-DC Converters from Measurements of Their Harmonic Transfer Function

Lefteriu, SandaIMT Lille Douai
Keywords: Power systems, Time-varying systems, Infinite-dimensional systems
Abstract: Power converters rely on semiconductor devices (transistors and/or diodes) acting as switches opening and closing periodically, hence they can be analyzed as periodic switched linear systems. The periodic behavior makes it possible to model them via the Harmonic Transfer Function (HTF). The HTF contains an infinite number of transfer functions, relating to each harmonic, but for converters operating in continuous current mode, a limited number of harmonics yields satisfactory results. This extended abstract aims at recovering a state-space model in a system identification sense from frequency-domain measurements that are physically realizable. After analyzing the open-loop response to a range of small-signal inputs with the FFT, the measurements of the HTF are obtained. This data set is used in the Loewner framework to create a descriptor-form continuous model. The advantage of the Loewner framework is that the quantities involved can be expressed in terms of generalized controllability and observability matrices. Hence, the similarity transformation is the extended observability matrix, and an optimization problem can be set up and solved iteratively for recovering the Fourier coefficients of the converter's state-space matrices and, consequently, the full description of the periodic system.
VI113-06
Reachability Analysis, Verification , and Abstraction of Hybrid Systems Regular Session
Chair: Wisniewski, RafalAalborg University
Co-Chair: Sigalotti, MarioInria
Paper VI113-06.1 
PDF · Video · Reachable Sets for a 3D Accidentally Symmetric Molecule

Boscain, Ugo V.DR2, CNRS, CMAP, Ecole Polytechnique
Pozzoli, EugenioINRIA, LJLL, Sorbonne Université
Sigalotti, MarioInria
Keywords: Reachability analysis, verification and abstraction of hybrid systems
Abstract: In this paper we study the controllability properties of the quantum rotational dynamics of a 3D symmetric molecule, with electric dipole moment not collinear to the symmetry axis of the molecule (that is, an accidentally symmetric-top). We control the dynamics with three orthogonally polarized electric fields. When the dipole has a nonzero component along the symmetry axis, it is known that the dynamics is approximately controllable. We focus here our attention to the case where the dipole moment and the symmetry axis are orthogonal (that is, an orthogonal accidentally symmetric-top), providing a description of the reachable sets.
Paper VI113-06.2 
PDF · Video · Safety Verification for Impulsive Systems

Feketa, PetroChristian-Albrechts-University Kiel
Bogomolov, SergiyAustralian National University
Meurer, ThomasChristian-Albrechts-University Kiel
Keywords: Reachability analysis, verification and abstraction of hybrid systems
Abstract: The problem of safety verification for a subclass of hybrid systems, namely for impulsive systems with fixed moments of jumps is considered. Sufficient conditions are derived for the safety of impulsive systems whose continuous dynamics may steer the state outside the safe region. For this purpose auxiliary barrier certificates with nonlinear rates are introduced and equipped with appropriate dwell-time conditions which restrict the upper bound for the inter-jump interval in order to ensure the desired safety property. The proposed approach is demonstrated by performing safety verification of linear and nonlinear impulsive systems.
Paper VI113-06.3 
PDF · Video · Reach-Set Estimation for DAE Systems under Uncertainty and Disturbances Using Trajectory Sensitivity and Logarithmic Norm

Geng, SijiaUniversity of Michigan
Hiskens, Ian A.University of Michigan
Keywords: Reachability analysis, verification and abstraction of hybrid systems, Bounded error identification, Continuous time system estimation
Abstract: Trajectory sensitivity analysis is useful for analyzing the dynamic behaviour of differential-algebraic equation (DAE) systems under uncertain initial conditions and/or parameters. However, the approximate trajectories obtained using trajectory sensitivities are not accompanied by explicit error bounds. In this paper, we provide an efficient method to obtain a numerical error bound for the first-order trajectory approximation. This approach uses second-order trajectory sensitivities. A theoretical result quantifying the excursion of trajectories induced by uncertain initial conditions and external disturbances is derived based on the logarithmic norm, and is extended to DAE systems. Although this result itself provides a guaranteed over-approximation of the reach-set of nonlinear DAE systems, by combining this result with the efficient bound obtained from trajectory sensitivities, we are able to provide a much less conservative reach-set estimate for systems under uncertain initial conditions and/or parameters, and external disturbances.
Paper VI113-06.4 
PDF · Video · Compositional Construction of Finite MDPs for Continuous-Time Stochastic Systems: A Dissipativity Approach (I)

Nejati, AmenehTechnical University of Munich (TUM)
Zamani, MajidUniversity of Colorado Boulder
Keywords: Reachability analysis, verification and abstraction of hybrid systems, Control of networks, Synthesis of stochastic systems
Abstract: This paper provides a compositional scheme based on dissipativity approaches for constructing finite abstractions of continuous-time continuous-space stochastic control systems. The proposed framework enjoys the structure of the interconnection topology and employs a notion of stochastic storage functions, that describe joint dissipativity-type properties of subsystems and their abstractions. By utilizing those stochastic storage functions, one can establish a relation between continuous-time continuous-space stochastic systems and their finite counterparts while quantifying probabilistic distances between their output trajectories. Consequently, one can employ the finite system as a suitable substitution of the continuous-time one in the controller design process with a guaranteed error bound. In this respect, we first leverage dissipativity-type compositional conditions for the compositional quantification of the distance between the interconnection of continuous-time continuous-space stochastic systems and that of their discrete-time (finite or infinite) abstractions. We then consider a specific class of stochastic affine systems and construct their finite abstractions together with their corresponding stochastic storage functions. We illustrate the effectiveness of the proposed techniques by applying them to a physical case study.
Paper VI113-06.5 
PDF · Video · Symbolic Supervisory Control of Periodic Event-Triggered Control Systems

Ren, WeiKTH Royal Institute of Technology
Dimarogonas, Dimos V.KTH Royal Institute of Technology
Keywords: Reachability analysis, verification and abstraction of hybrid systems, Discrete event modeling and simulation, Supervisory control and automata
Abstract: This paper studies supervisory control of periodic event-triggered control (PETC) systems based on the construction of symbolic abstractions. To this end, we first construct symbolic abstractions for PETC systems, and establish feedback refinement relation from the PETC system to its symbolic models. Here, the constructed symbolic models are represented by the form of discrete event systems (DESs), including extended finite state machines, finite state machines, and classic DESs. With the constructed symbolic models, we study the supervisory control of PETC systems to achieve the desired specification. Since the constructed symbolic models are nondeterministic, we first transfer the symbolic models into deterministic versions, and then verify the existence of the supervisor. Finally, the obtained results are illustrated via a numerical example.
Paper VI113-06.6 
PDF · Video · Anomaly Detection of Markov Processes with Evolution Equation and Moments

Wisniewski, RafalAalborg University
Bujorianu, Luminita-ManuelaUniversity of Strathclyde
Keywords: Reachability analysis, verification and abstraction of hybrid systems, Fault detection and diagnosis, Synthesis of stochastic systems
Abstract: Our departure point is the evolution equation of a Markov process. It describes the changes in the transition probability as time passes. We compare the transition probability for a priori model with the actual transition probability of the observed process to detect a mismatch between the expected and the measured data. To translate this idea into an algorithm, we characterize the involved measures by their moments. Specifically, a linear dynamic system is put forward that describes the evolution of moments. As the last result, we define a moment divergence as the means of computing the distance between two sequences of moments. We see the work as a step towards merging model-driven and data-driven concepts in control engineering. To elucidate the concepts introduced, we have incorporated several simple examples.
VI113-07
Stability and Stabilization of Hybrid Systems Regular Session
Chair: Teel, Andrew R.Univ. of California at Santa Barbara
Co-Chair: Liberzon, DanielUniv. of Illinois at Urbana-Champaign
Paper VI113-07.1 
PDF · Video · Average Dwell-Time Conditions for Input-To-State Stability of Impulsive Systems

Bachmann, PatrickUniversity of Kaiserslautern
Bajcinca, NaimUniversity of Kaiserslautern
Keywords: Stability and stabilization of hybrid systems
Abstract: This paper provides sufficient conditions for input-to-state stability of impulsive control systems on Banach spaces. The derived conditions determine average dwell-time constraints for a candidate Lyapunov function parametrized by a class of nonlinear rate functions in order to guarantee the ISS property. Thereby, we consider a generalized case with unstable continuous flow maps and assume the jumps, rather than the continuous flow to induce a stabilizing influence on the system dynamics of the impulsive system. Compared to some well-known related and recent results in the literature, such as fixed dwell-time conditions, the obtained conditions are more general, while offering a higher flexibility in the choice of candidate Lyapunov functions.
Paper VI113-07.2 
PDF · Video · A Lyapunov-Razumikhin Condition of ISS for Switched Time-Delay Systems under Average Dwell Time Commutation

Zhang, JunfengHangzhou Dianzi University
Efimov, DenisInria
Keywords: Stability and stabilization of hybrid systems
Abstract: A condition of ISS is proposed for nonlinear time-delay systems based on the Lyapunov-Razumikhin theory, which allows the rate of convergence to be evaluated. Then, this condition is used for ISS analysis of switched nonlinear time-delay systems with average dwell time switching. Finally, one example is given to verify the effectiveness of theoretical ndings.
Paper VI113-07.3 
PDF · Video · Quasi-Integral-Input-To-State Stability for Switched Nonlinear Systems

Russo, AntonioUniversità Della Campania
Liu, ShenyuUniversity of California, San Diego
Liberzon, DanielUniv. of Illinois at Urbana-Champaign
Cavallo, AlbertoUniversity of Campania Luigi Vanvitelli
Keywords: Stability and stabilization of hybrid systems
Abstract: In this paper we introduce and give sufficient conditions for the quasi-iISS property for switched nonlinear system under dwell-time switching signals. Unlike previous works, our dwell-time bound does not rely on the knowledge of the state but it relies only on the system initial condition and the bound on the input energy. We prove, through a counterexample, that knowledge of the system initial state and bound on input energy is necessary for the estimation of a dwell-time that guarantees quasi-iISS for the switched system. An illustrative example is also included.
Paper VI113-07.4 
PDF · Video · Stability of Uncertain Piecewise-Affine Systems with Parametric Dependence

Massioni, PaoloINSA De Lyon
Bako, LaurentEcole Centrale De Lyon
Scorletti, GerardEcole Centrale De Lyon
Keywords: Stability and stabilization of hybrid systems
Abstract: This paper proposes a numerical approach to the stability analysis for a class of piecewise-affine systems with (possibly time-varying) parameter-dependent cells and dynamics. This class of model aims at allowing a better modelling of time-varying or parameter-varying nonlinearities of physical phenomena such as dry friction. We form the stability certification problem as the one of finding a Lyapunov function that is parameterised as a polynomial function of the variable parameter. The application of the well-known Lyapunov stability together with the use of the generalised S-procedure reduces the problem to checking whether a certain set of matrices has the sum-of-squares property. The latter can be solved using well-documented numerical solvers, and we provide two examples of successful applications at the end of the paper.
Paper VI113-07.5 
PDF · Video · Lie-Algebraic Criterion for Stability of Switched Differential-Algebraic Equations

Harivanam, PhaniIndian Institute of Technology Bombay
Pal, DebasattamIndian Institute of Technology Bombay
Keywords: Stability and stabilization of hybrid systems
Abstract: In this paper, we prove a Lie algebraic result for stability of switched DAEs with a common descriptor matrix (common E matrix). We first show that if a switched DAE with a common descriptor matrix is asymptotically stable, then it is also globally uniformly exponentially stable. We then show that switched DAEs with common descriptor matrix and consistent block upper triangular structure is globally uniformly exponentially stable if and only if the switched DAEs corresponding to the diagonal blocks are globally uniformly exponentially stable. Finally, we show that a switched DAE with common descriptor matrix, stable and impulse free DAE subsystems, is globally uniformly exponentially stable (GUES) if there exists an invertible matrix N such that the Lie algebra generated by NE,NA_i is solvable.
Paper VI113-07.6 
PDF · Video · A Semi-Global Hybrid Sensorless Observer for Permanent Magnet Synchronous Machines with Unknown Mechanical Model

Bosso, AlessandroAlma Mater Studiorum - University of Bologna
Azzollini, Ilario AntonioUniversity of Bologna
Tilli, AndreaUniversity of Bologna
Keywords: Stability and stabilization of hybrid systems, Adaptive observer design
Abstract: In this paper, we present a hybrid sensorless observer for Permanent Magnet Synchronous Machines, with no a priori knowledge of the mechanical dynamics and without the typical assumption of constant or slowly-varying speed. Instead, we impose the rotor speed to have a constant (unknown) sign and a non-zero magnitude at all times. For the design of the proposed scheme, we adopt meaningful Lie group formalism to describe the rotor position as an element of the unit circle. This choice, however, leads to a non-contractible state space, and therefore it introduces topological constraints that complicate the achievement of global/semi-global and robust results. In this respect, we show that the proposed observer, which augments a recent continuous-time solution, achieves semi-global practical asymptotic stability by periodically resetting the estimates. As highlighted in the simulation results, the novel hybrid strategy leads to improved transient performance, notably without any modification of the gains employed in the continuous-time version. These features motivate to augment the observer with a discrete-time identifier, leading to significantly faster rotor flux reconstruction.
Paper VI113-07.7 
PDF · Video · Lyapunov Characterizations of Input-To-State Stability for Discrete-Time Switched Systems Via Finite-Step Lyapunov Functions

Sharifi, MaryamTehran University
Noroozi, NavidOtto-von-Guericke-Universität Magdeburg
Findeisen, RolfOtto-von-Guericke-Universität Magdeburg
Keywords: Stability and stabilization of hybrid systems, Control over networks
Abstract: This paper addresses Lyapunov characterizations of input-to-state stability for nonlinear switched discrete-time systems via finite-step Lyapunov functions with respect to closed sets. The use of finite-step Lyapunov functions permits not-necessarily input-to-state stable systems in the systems family, while input-to-state stability of the resulting switched system is ensured. The result is generally presented for systems under arbitrary switching. It additionally covers the case of constrained switchings. We illustrate the effectiveness of our results by application to networked control systems with periodic scheduling policies under a priori known and dwell time-based switching mechanism.
Paper VI113-07.8 
PDF · Video · Stability of Charge-Pump Phase-Locked Loops: The Hold-In and Pull-In Ranges

Kuznetsov, NikolaySaint-Petersburg State Univ
Matveev, Alexey S.St.Petersburg Univ
Yuldashev, MaratSaint Petersburg State University
Yuldashev, RenatSt. Petersburg State University
Bianchi, GiovanniAdvantest Europe GMBH
Keywords: Stability and stabilization of hybrid systems, Dynamic Networks, Nonlinear system identification
Abstract: The problem of design and analysis of synchronization control circuits is a challenging task for many applications: satellite navigation, digital communication, wireless networks, and others. In this article the Charge-Pump Phase-Locked Loop (CP-PLL) electronic circuit, which is used for frequency synthesis and clock generation in computer architectures, is studied. Analysis of CP-PLL is not trivial: full mathematical model, rigorous definitions, and analysis still remain open issues in many respects. This article is devoted to development of a mathematical model, taking into account engineering aspects of the circuit, interpretation of core engineering problems, definition in relation to mathematical model, and rigorous analysis.
Paper VI113-07.9 
PDF · Video · Lyapunov-Based Singular Perturbation Results in the Framework of Hybrid Systems

Wang, Xue-FangDalian University of Technology
Liu, Kun-ZhiDalian University of Technology
Sun, Xi-MingDalian University of Technology
Teel, Andrew R.Univ. of California at Santa Barbara
Keywords: Stability and stabilization of hybrid systems, Hybrid and switched systems modeling
Abstract: Stability properties of singularly perturbed hybrid systems are investigated via Lyapunov functions with assistance from the invariance principle. Both continuously differentiable Lyapunov functions and non-smooth Lyapunov functions are considered. In each case, under appropriate assumptions, uniform asymptotic stability and uniform global asymptotic stability are established. An estimate of the basin of attraction is given for the former property. Two examples are given to illustrate the proposed theoretical results based on continuously differentiable Lyapunov functions. In addition, one example for switched learning inclusions with unstable modes is given to show the effectiveness of the results obtained based on non-smooth Lyapunov functions.
Paper VI113-07.10 
PDF · Video · On the Stability of Discrete-Time Linear Switched Systems in Block Companion Form

De Iuliis, VittorioUniversità Degli Studi Dell'Aquila
D'Innocenzo, AlessandroUniversità Degli Studi Di L'Aquila
Germani, AlfredoUniversity of L'Aquila
Manes, CostanzoUniversità Dell'Aquila
Keywords: Stability and stabilization of hybrid systems, Hybrid and switched systems modeling
Abstract: Inspired by some insightful results on the delay-independent stability of discrete-time systems with time-varying delays, in this work we study the arbitrary switching stability for some classes of discrete-time switched systems whose dynamic matrices are in block companion form. We start from the special family of block companion matrices whose first block-row is made of permutations of nonnegative matrices, deriving a simple necessary and sufficient condition for its arbitrary switching stability. Then we relax both these assumptions, at the expense of introducing some conservatism. Some consequences on the computation of the Joint Spectral Radius for the aforementioned families of matrices are illustrated.
Paper VI113-07.11 
PDF · Video · Omega-Limit Sets and Robust Stability for Switched Systems with Distinct Equilibria

Baradaran Hosseini, MatinaUniversity of California, Santa Barbara
Teel, Andrew R.Univ. of California at Santa Barbara
Keywords: Stability and stabilization of hybrid systems, Hybrid and switched systems modeling
Abstract: This work characterizes the asymptotic behavior that results from switching among a family of asymptotically stable systems with distinct equilibria when the switching frequency satisfies an average dwell-time constraint with a small average rate. The asymptotic characterization is in terms of the omega limit set of an associated ideal hybrid system containing an average dwell-time automaton with the rate parameter set equal to zero. This set is globally asymptotically stable for the ideal system. The actual switched system, together with small disturbances, constitutes a small perturbation of this ideal system, resulting in semi-global, practical asymptotic stability.
Paper VI113-07.12 
PDF · Video · Output-Feedback Stabilization for Descriptor Markovian Jump Systems with Generally Uncertain Transition Rates

Park, In SeokPohang University of Science and Technology
Park, Chan-eunPOSTECH
Park, PooGyeonPohang Univ. of Sci. & Tech
Keywords: Stability and stabilization of hybrid systems, Stochastic hybrid systems
Abstract: This paper presents a dynamic output-feedback stabilization problem of descriptor Markovian jump systems with generally uncertain transition rates. First, a new necessary and sufficient condition to relax inequalities including generally uncertain transition rates is introduced. For the closed-loop systems with a dynamic output-feedback controller, the stabilization criterion is achieved as non-convex matrix inequalities. For the obtained criterion, this paper gives an improved necessary and sufficient condition in terms of linear matrix inequalities under completely known transition rates. Then, the proposed condition is extended for the descriptor Markovian jump systems with generally uncertain transition rates. To show the validity of the proposed control, a numerical example is given.
Paper VI113-07.13 
PDF · Video · A Positive Real Lemma for Singular Hybrid System

Park, Chan-eunPOSTECH
Park, In SeokPohang University of Science and Technology
Kwon, Nam KyuYeungnam University
Park, PooGyeonPohang Univ. of Sci. & Tech
Keywords: Stochastic hybrid systems, Stability and stabilization of hybrid systems
Abstract: This paper introduces a positive real lemma for continuous-time singular hybrid systems. The necessary and sufficient condition of stochastic admissibility and strictly passivity for the singular hybrid systems is obtained in terms of linear matrix inequalities. The sufficient condition is driven by using mode-dependent Lyapunov function. In this step, two slack variables are inserted to make the proposed condition be necessary and sufficient condition in terms of strict linear matrix inequalities. Then, to prove the necessary condition, the positive real lemma for the hybrid system is proposed. Since the admissible singular system can be reformulated into stable normal system, the positive real lemma for the hybrid system holds. Thus, we give a necessary condition by constructing the solution of the proposed lemma from that of the hybrid systems.
VI113-08
Supervisory Control and Analysis of Discrete Event Systems Regular Session
Chair: Giua, AlessandroUniversity of Cagliari, Italy
Co-Chair: Moor, ThomasFriedrich-Alexander Universität Erlangen-Nürnberg
Paper VI113-08.1 
PDF · Video · Controlled Microparticle Separation Using Whispering Gallery Mode Forces

Chang, YuheBoston University
Svitelskiy, OleksiyGorden College
Ekinci, KamilBoston University
Andersson, SeanBoston University
Keywords: Discrete event modeling and simulation, Distributed control and estimation, Filtering and smoothing
Abstract: There is a wide variety of applications that require sorting and separation of micro-particles from a large cluster of similar objects. Existing methods can distinguish micro-particles by their bulk properties, such as their size, density, and electric polarizability. These methods, however, are not selective with respect to the individual geometry of the particles. In this work, we focus on the use of a resonance effect between a microparticle and an evanescent light field known as the Whispering Gallery Mode (WGM) force. The WGM force is highly sensitive to the radius of the particle and is both controllable and tunable. In this paper, we explore through simulation the design of a WGM-based device for micro-particle separation. In this device, particles flow in through an inlet and are carried over two actuation regions given by waveguides carrying laser light to generate the evanescent field. Particles are observed by a camera, allowing for feedback control on the power of the lasers. While the basic control structure is simple, there are several challenges, including unknown disturbances to the fluid flow, limited laser power, and uni-directional control over each actuation region. We combine Expectation Maximization with Kalman filtering to both estimate the unknown disturbance and filter the measurements into a position estimate. We then develop simple hybrid controllers and compare them to the ideal setting (without any constraints) based on a Linear–Quadratic–Gaussian (LQG) control approach.
Paper VI113-08.2 
PDF · Video · Distributed Multirobot Path Planning in Unknown Maps Using Petri Net Models

Mahulea, CristianUniversity of Zaragoza
Montijano, EduardoUniversidad De Zaragoza
Kloetzer, MariusTechnical University of Iasi
Keywords: Discrete event modeling and simulation, Petri nets, Multi-agent systems
Abstract: This paper considers the path planning problem in multirobot systems with an unknown environment. The robots' mission is given as a Boolean formula on the final states. We assume that the robots have partial knowledge of the environment and they are able to estimate the environment using a recursive Bayes estimator. Furthermore, they communicate between them if they are at a distance smaller than a given threshold in order to improve their own estimation. Each robot will solve an optimization problem based on the Petri net model of the environment and it will move accordingly. We provide an algorithm to be iterated by each robot and we evaluate the results by simulation.
Paper VI113-08.3 
PDF · Video · Queueing Network Realization of an Epidemiological Model for Efficient Evaluation of Computer Transmitted Infections

Wu, Neng EvaBinghamton Univ
Montague, JoshuaState University of New York at Binghamton
Van Ornam, DrakeBinghamton University
Sarailoo, MortezaBinghamton University
Bay, John S.State University of New York at Binghamton
Keywords: Discrete event modeling and simulation, Queueing systems and performance model                                       , Secure networked control systems
Abstract: This paper reexamines an epidemiological model with 4 population groups (vigilant/non-vigilant susceptible/infectious) built to study the effect of user vigilance on computer transmitted infections (CTIs) in computer networks. The model serves as an example through which a model conversion process is delineated, which aims at enhancing computational efficiency in the evaluation of the global prevalence of CTIs. More specifically, the conventional node-centric networked Markov chain (NCMC) is remodeled as a population-centric Markov chain (PCMC) to reduce the state-space size from an exponential to a polynomial function of the number of computing nodes N in a strongly connected network, where external attack and internal spread processes are aggregated. The PCMC is then realized as a closed queueing network of 4 M/M/N/N queueing nodes, corresponding to the 4 population groups. The results of evaluating the evolution of mean populations for the 4-population network of up to 150,000 computing nodes show that the queueing network realization slows the growth of computational complexity from exponential to linear with respect to the network size without resorting to mean field approximations. The paper briefly discusses on how the queueing network framework can accommodate node-centric Markov chains (NCMCs) of arbitrary directed networks of heterogeneous nodes, and its potential to significantly reduce the complexity in the evaluation of mean population dynamics for the more general class of large networks.
Paper VI113-08.4 
PDF · Video · On Opacity Verification for Discrete-Event Systems

Masopust, TomasCzech Academy of Sciences and Palacky University in Olomouc
Balun, JiříPalacký University in Olomouc
Keywords: Discrete event modeling and simulation, Supervisory control and automata, Diagnosis of discrete event and hybrid systems
Abstract: Opacity is an information flow property characterizing whether a system reveals its secret to an intruder. Verification of opacity for discrete-event systems modeled by automata is in general a hard problem. We discuss the question whether there are structural restrictions on the system models for which the opacity verification is tractable. We consider two kinds of automata models: (i) acyclic automata, and (ii) automata where all cycles are only in the form of self-loops. In some sense, these models are the simplest models of (deadlock-free) systems. Although the expressivity of such systems is weaker than the expressivity of linear temporal logic, we show that the opacity verification for these systems is still hard.
Paper VI113-08.5 
PDF · Video · Communication Policies in Heterogeneous Multi-Agent Systems in Partially Known Environments under Temporal Logic Specifications

Keroglou, ChristoforosUniversity of Michigan, Ann Arbor
Dimarogonas, Dimos V.KTH Royal Institute of Technology
Keywords: Discrete event modeling and simulation, Supervisory control and automata, Multi-agent systems
Abstract: In this paper, we explore communication protocols between two or more agents in an initially partially known environment.We assume two types of agents (A and B), where an agent of Type A constitutes an information source (e.g., a mobile sensor) with its own local objective expressed in temporal logic, and an agent of Type B constitutes an agent that accomplishes its own mission (e.g., search and rescue mission) also expressed in temporal logic. An agent of Type B requests information from an agent of Type A to update its knowledge about the environment. In this work, we develop an algorithm that is able to verify if a communication protocol exists, for any possible initial plan executed by an agent of Type B.
Paper VI113-08.6 
PDF · Video · Structural Characterization of Controllability in Timed Continuous Petri Nets Using Invariant Subspaces

Arzola, CésarUniversidad De Zaragoza
Vazquez, Carlos RenatoITESM Campus Guadalajara
Silva, ManuelUniversidad De Zaragoza
Ramirez-Trevino, AntonioCINVESTAV-IPN
Keywords: Petri nets, Hybrid and switched systems modeling, Reachability analysis, verification and abstraction of hybrid systems
Abstract: This work deals with the controllability analysis in Timed Continuous Petri Nets (TCPNs) under infinite server semantics, a fluid relaxation that can model highly populated Discrete Event Systems. Here, the full rank-controllability property is defined, ensuring that the TCPN is controllable over the equilibrium markings in each of the regions of its reachability space. This allows forcing the TCPN systems to work at interesting operation points such as maximum production states, safety regions, to mention a few. Herein two structural conditions for full rank-controllability, one necessary and the other sufficient, are introduced, avoiding the enumeration of all the configurations required in other approaches. Finally, based on this, a polynomial algorithm to test the full rank-controllability is provided.
Paper VI113-08.7 
PDF · Video · A Monte-Carlo Tree Search Based Tracking Control Approach for Timed Petri Nets

Fritz, RaphaelUniversity of Kaiserslautern
Krebs, NicoUniversity of Kaiserslautern
Zhang, PingUniv of Kaiserslautern
Keywords: Petri nets, Particle filtering/Monte Carlo methods
Abstract: In this paper, an approach for the tracking control problem for discrete event systems modeled by timed Petri nets (TPN) is proposed. The approach applies the Monte-Carlo Tree Search to the tracking control problem for TPN to find a firing sequence from an initial marking to the desired destination marking that minimizes the required duration. The proposed tracking control method randomly searches a small part of the reachability graph and incrementally constructs a search tree to find the optimal solution. This reduces the computational effort and allows the approach to solve the tracking control problem for larger systems. The approach has capabilities for deadlock avoidance and can be applied to a wide range of control problems like reachability analysis, fault-tolerant control and scheduling problems.
Paper VI113-08.8 
PDF · Video · Regulation Control in Interpreted Petri Nets under Partial Observation

Jiménez-Ochoa, ItaliaCINVESTAV
Guevara-Lozano, DanielCINVESTAV
Vazquez, Carlos RenatoITESM Campus Guadalajara
Ramirez-Trevino, AntonioCINVESTAV-IPN
Keywords: Petri nets, Supervisory control and automata, Event-based control
Abstract: This paper addresses the regulation control problem for discrete event systems (DES) under partial information. In this approach, the system to be controlled, named the plant, and the required behavior, named the specification, are both represented as Petri nets (PNs) with input and output symbols. The goal is to synthesize a controller that indicates input symbols to the plant in order to reach an state where the output is equal to that of the specification. To achieve this goal, the only information available to the controller is the plant output, i.e., the controller does not know the exact state of the plant. In this work, a control methodology is proposed to synthesize regulation controllers under this setting.
Paper VI113-08.9 
PDF · Video · Simulation with Qualitative Models in Reduced Tensor Representations

Müller-Eping, ThorstenFraunhofer Institute for Solar Energy Systems - ISE
Lichtenberg, GerwaldHamburg University of Applied Sciences
Keywords: Quantized systems, Supervisory control and automata, Stochastic system identification
Abstract: The paper proposes simulation algorithms for a new tensor representation of qualitative models based on stochastic automata. We show that storing the transition probabilities of the stochastic automaton in tensor formats will help to break the curse of dimensionality, i.e. to overcome the storage complexity problem of the automaton that occurs due to the exponential growth in the quantity of automata transitions when the number of input, state and output signals of the underlying discrete-time system is high. In addition, we present the application of a modern tensor optimization method for the completion of qualitative models identified by data-driven black-box approaches and thus suffering from the problem of unobserved sets of training data.
Paper VI113-08.10 
PDF · Video · Maximal Permissiveness of Modular Supervisory Control Via Multilevel Structuring

Komenda, JanAcademy of Sciences of Czech Republic
Masopust, TomasCzech Academy of Sciences and Palacky University in Olomouc
van Schuppen, Jan H.Van Schuppen Control Research
Keywords: Supervisory control and automata
Abstract: Modular supervisory control is motivated by the gain in complexity of control synthesis of supervisors. Sufficient conditions for maximal permissiveness of supervisors include mutual controllability and mutual normality. In this paper, we show how these conditions can be weakened. Namely, we can relax the requirement that the conditions hold for all pairs of components by putting the tuples of plants that do not satisfy the given condition for maximal permissiveness into different groups on an intermediate level of abstraction.
Paper VI113-08.11 
PDF · Video · An Efficient Algorithm for the Computation of the Controllability Prefix of *-Languages (I)

Moor, ThomasFriedrich-Alexander Universität Erlangen-Nürnberg
Schmidt, Klaus WernerMiddle East Technical University
Schmuck, Anne-KathrinMax Planck Institute for Software Systems
Keywords: Supervisory control and automata
Abstract: Given a plant and a specification, both represented as formal languages, the controllability prefix is defined as the set of event sequences from which on a supervisor can control the plant according to the specification. The controllability prefix was first introduced in the context of omega-languages, where it plays a crucial role in the solution of the supervisory controller synthesis problem. In the present paper, we address the controllability prefix for *-languages. In our discussion, we (a) present a novel characterisation of the supremal controllable and relatively closed sublanguage in terms the controllability prefix; we (b) derive a fixpoint characterisation of winning states from a game theoretic interpretation of a specific state feedback synthesis problem; and (c) we establish a one-to-one correspondence between winning states and the controllability prefix. In summary, we obtain an efficient algorithm for the computation of the controllability prefix.
Paper VI113-08.12 
PDF · Video · On-Line Synthesis of Permissive Supervisors for Partially Observed Discrete Event Systems under scLTL Constraints (I)

Sakakibara, AmiOsaka University
Ushio, ToshimitsuOsaka Univ
Keywords: Supervisory control and automata
Abstract: We consider a supervisory control problem of a discrete event system (DES) under partial observation, where a control specification is given by a fragment of linear temporal logic. We design an on-line supervisor that dynamically computes its control action with the complete information of the product automaton of the DES and an acceptor for the specification. The concepts of controllability and observability are defined by means of a ranking function defined on the product automaton, which decreases its value if an accepting state of the product automaton is being approached. The proposed on-line control scheme leverages the ranking function and an energy function, which represents a time-varying permissiveness level. As a result, the on-line supervisor achieves the specification, being aware of the tradeoff between its permissiveness and acceptance of the specification, if the product automaton is controllable and observable.
Paper VI113-08.13 
PDF · Video · Instant Detectability of Discrete-Event Systems

Zhang, KuizeKTH Royal Institute of Technology
Giua, AlessandroUniversity of Cagliari, Italy
Keywords: Supervisory control and automata, Diagnosis of discrete event and hybrid systems
Abstract: Detectability is a basic property that describes whether an observer can use the current and past values of an observed output sequence produced by a system to reconstruct its current state. We consider particular properties called instant strong detectability and instant weak detectability, where the former implies that for each possible infinite observed output sequence each prefix of the output sequence allows reconstructing the current state, the latter implies that some infinite observed output sequence (if it exists) satisfies that each of its prefixes allows reconstructing the current state. For discrete-event systems modeled by finite-state automata, we give a linear-time verification algorithm for the former in the size of an automaton, and also give a polynomial-time verification algorithm for the latter.
Paper VI113-08.14 
PDF · Video · Control of Timed Discrete Event Systems with Ticked Linear Temporal Logic Constraints (I)

Kinugawa, TakumaOsaka University
Hashimoto, KazumuneKeio University
Ushio, ToshimitsuOsaka Univ
Keywords: Supervisory control and automata, Discrete event modeling and simulation
Abstract: This paper presents a novel method of synthesizing a controller of a timed discrete event system(TDES), introducing a novel linear temporal logic(LTL), called ticked LTLf. The ticked LTLf is given as an extension to LTLf, where the semantics is defined over a finite execution sequence. Differently from the standard LTLf, the formula is defined as a variant of metric temporal logic formula, where the temporal properties are described by counting the number of tick in the execution sequence of the TDES. Moreover, we provide a scheme that encodes the problem into a suitable one that can be solved by an integer linear programming (ILP). The effectiveness of the proposed approach is illustrated through a numerical example of a path planning.
Paper VI113-08.15 
PDF · Video · A Reactive Synthesis Approach to Supervisory Control of Terminating Processes (I)

Schmuck, Anne-KathrinMax Planck Institute for Software Systems
Moor, ThomasFriedrich-Alexander Universität Erlangen-Nürnberg
Schmidt, Klaus WernerMiddle East Technical University
Keywords: Supervisory control and automata, Event-based control
Abstract: This paper establishes a connection between supervisory control theory (SCT) and reactive synthesis (RS) in the situation where both the plant and the specification are modeled by *-languages, i.e., formal languages over finite words. In particular, we show that the deterministic finite automaton G typically used in SCT to construct a maximally permissive supervisor f for a plant language L w.r.t. a specification language E, can be interpreted as a two-player game which allows to solve the considered synthesis problem by a two-nested fixed-point algorithm in the mu-calculus over G. The resulting game turns out to be a cooperative Büchi-type game which allows for a maximally permissive solution in the particular context of SCT. This is surprising, as classical Büchi games do not have this property.
Paper VI113-08.16 
PDF · Video · Enforcing Opacity in Modular Systems

Zinck, GraemeMount Allison University
Ricker, LaurieMount Allison University
Marchand, HerveIRISA/INRIA Rennes
Loïc, HélouëtINRIA
Keywords: Supervisory control and automata, Multi-agent systems
Abstract: In discrete-event systems, the opacity of a secret ensures that some behaviors or states cannot be inferred with certainty from partial observation of the system. Enforcing opacity in a discrete-event system, encoded by a finite labelled transition system (LTS), is a way to avoid information leakage. Checking opacity is decidable but costly (EXPTIME in the worst cases). This paper addresses opacity for modular systems in which every module, represented by an LTS, has to protect its own secret (a set of secret states) w.r.t. a local attacker. Once the system is composed, we assume a coalition between the attackers that share their local view (called the global attacker). Assuming the global attacker can observe all interactions between modules, we provide a reduced-complexity opacity verification technique and an algorithm for constructing local controllers that enforces opacity for each secret separately.
VI114
Systems and Signals - Stochastic Systems
VI114-02 Stochastic Systems, Stochastic Control and Adaptive Control, Numerical Methods, Deep Learning and Data Science   Invited Session, 12 papers
VI114-03 Stochastic Control and Game Theory   Regular Session, 8 papers
VI114-04 Stochastic Systems Estimation and Filtering   Regular Session, 22 papers
VI114-05 Stochastic System Identification   Regular Session, 5 papers
VI114-02
Stochastic Systems, Stochastic Control and Adaptive Control, Numerical
Methods, Deep Learning and Data Science
Invited Session
Chair: Pasik-Duncan, BozennaUniv. of Kansas
Co-Chair: Baras, John S.Univ. of Maryland
Organizer: Pasik-Duncan, BozennaUniv. of Kansas
Paper VI114-02.1 
PDF · Video · Series Solution of Stochastic Dynamic Programming Equations (I)

Krener, Arthur JNaval Postgraduate School
Keywords: Stochastic control and game theory
Abstract: In this paper we consider discrete time stochastic optimal control problems over infinite and finite time horizons. We show that for a large class of such problems the Taylor polynomials of the solutions to the associated Dynamic Programming Equations can be computed degree by degree.
Paper VI114-02.2 
PDF · Video · Filterless Least-Squares Based Adaptive Stochastic Continuous-Time Nonlinear Control (I)

Li, WuquanLudong University
Krstic, MiroslavUniv. of California at San Diego
Keywords: Stochastic system identification, Synthesis of stochastic systems
Abstract: In continuous-time system identification and adaptive control, the least-squares parameter estimation algorithm has always been used with regressor filtering, in order to avoid using time-derivatives of the measured state. Filtering adds to the dynamic order of the identifier and affects its performance. We solve the problem of filterless least-squares-based adaptive control for stochastic strict-feedback nonlinear systems with an unknown parameter in the drift term. The novel ingredient in our least-squares identification is that the update law for the parameter estimate is not a simple integrator but it also incorporates a feedthrough effect, namely, the parameter estimator is of relative degree zero (rather than one) relative to the update function. The feedthrough in the update law is a carefully designed nonlinear function, which incorporates the integration with respect to state (and not time) of the regressor function, the purpose of which is to eliminate the need for time-filtering of the regressor. Our backstepping design of the control law compensates the adverse effect of the noise (the Hessian nonlinear term, involving the diffusion nonlinearity, in the Lyapunov analysis) on the least-squares estimator. Such a controller also enables a construction of an single overall Lyapunov function, quadratic in the parameter error and quartic in the transformed state, to guarantee that the equilibrium at the origin of the closed-loop system is globally stable in probability and the states are regulated to zero almost surely.
Paper VI114-02.3 
PDF · Video · Ergodic Linear-Quadratic Control for a Two Dimensional Stochastic System Driven by a Continuous Non-Gaussian Noise (I)

Duncan, Tyrone E.Univ. of Kansas
Pasik-Duncan, BozennaUniv. of Kansas
Keywords: Stochastic control and game theory, Stochastic system identification, Learning for control
Abstract: In this paper an infinite time horizon (ergodic) quadratic cost control problem for a linear two dimensional stochastic system with a two dimensional Rosenblatt noise process is solved by providing an explicit expression to determine the optimal feedback. The system has some symmetry properties that allow for an explicit determination of an optimal control. The controls are the family of constant linear feedbacks which is known to be the natural family of controls for a Brownian motion noise to determine optimality. This family of constant linear feedback controls allows for practical implementation of the optimal control. Rosenblatt processes are continuous, non-Gaussian processes that have a long range dependence and a useful stochastic calculus and they are generated by double Wiener-It^o integrals with singular kernels. The long range dependence property of the Rosenblatt processes is a natural generalization from an important subfamily of (Gaussian) fractional Brownian motions. Long range dependent processes have been identified empirically in a significant variety of physical phenomena. An expression is obtained to determine explicitly the optimal ergodic control. The ergodic control result in this paper seems to be the first explicit ergodic control result for a multidimensional control system with a continuous, non-Gaussian noise. Furthermore it seems to be the first solution for a multi-dimensional game problem with a Rosenblatt noise.
Paper VI114-02.4 
PDF · Video · Distributed Planning in Mean-Field-Type Games (I)

Tembine, HamidouNew York University
Keywords: Stochastic control and game theory
Abstract: In this paper we study the problem of designing a collection of terminal payoff of mean-field type by interacting decision-makers to a specified terminal measure. We solve in a semi-explicit way a class of distributed planning in mean-field-type games with different objective functionals. We establish some relationships between the proposed framework, optimal transport theory, and distributed control of dynamical systems.
Paper VI114-02.5 
PDF · Video · PID Control of Nonlinear Stochastic Systems with Structural Uncertainties (I)

Zhang, JinkeChinese Academy of Sciences
Guo, LeiChinese Academy of Sciences
Keywords: Stochastic adaptive control, Nonlinear adaptive control, Synthesis of stochastic systems
Abstract: It is widely known that the classical PID (proportional-integral-derivative) controller still plays a dominating role in engineering control systems, and that most of the theoretical studies on PID control focus on linear deterministic systems. In this paper, we will extend the authors recent theoretical investigation by considering additional uncertainties in the input channel, and try to establish a theoretical foundation on the PID control for a class of high-dimensional nonlinear stochastic systems with structural uncertainties consisting of dynamics uncertainty, diffusion uncertainty and input channel uncertainty. We will construct a three dimensional parameter set based on the available information, so that under the classical PID control, the closed-loop control system can be globally stabilized with regulation error tending to zero in the mean square sense, as long as the three PID parameters are chosen from this set. We will further show that global stabilization and asymptotic regulation of a class of multi-agent uncertain nonlinear stochastic systems can also be achieved by uncoupled PID controllers of the agents.
Paper VI114-02.6 
PDF · Video · Prescribed-Time Mean-Square Nonlinear Stochastic Stabilization (I)

Li, WuquanLudong University
Krstic, MiroslavUniv. of California at San Diego
Keywords: Synthesis of stochastic systems, Stochastic control and game theory
Abstract: We solve the prescribed-time mean-square stabilization problem, providing the first feedback solution to a stochastic null-controllability problem for strict-feedback nonlinear systems with stochastic disturbances. Our non-scaling backstepping design scheme's key novel design ingredient is that, rather than employing ``blowing up" time-varying scaling of the backstepping coordinate transformation, we introduce, instead, a damping in the backstepping target systems which grows unbounded as time approaches the terminal time. With this approach, even for deterministic systems, a simpler controller results and the control effort is reduced compared to previous designs. We achieve prescribed-time stabilization in the mean-square sense.
Paper VI114-02.7 
PDF · Video · Minimal Feedback Optimal Control of Linear-Quadratic-Gaussian Systems: No Communication Is Also a Communication (I)

Maity, DipankarGeorgia Institute of Technology
Baras, John S.Univ. of Maryland
Keywords: Control under communication constraints, Control over networks, Stochastic control and game theory
Abstract: We consider a linear-quadratic-Gaussian optimal control problem where the sensor and the controller are remotely connected over a communication channel. The communication of the measurement from the sensor to the controller requires a certain cost which is augmented with the quadratic control cost. We formulate a control and communication co-design problem where we solve for the joint optimal pair of controller and transmitter. We emphasize on the fact that absence of measurement communication at any time instance also conveys certain information to the controller, and such implicit information should be taken into account while designing a controller. We decompose the problem into two subproblems to construct the optimal controller and the optimal transmitter. While the optimal controller can be constructed by solving a certain Riccati equation, the optimal transmitter can be found solving a certain dynamic programming problem. We fi rst characterize a sub-optimal solution for this dynamic program and then design an iterative algorithm to further improve the sub-optimal solution.
Paper VI114-02.8 
PDF · Video · Conversion of Certain Stochastic Control Problems into Deterministic Control Problems (I)

McEneaney, WilliamUniv of California, San Diego
Dower, Peter M.University of Melbourne
Keywords: Stochastic control and game theory
Abstract: A class of nonlinear, stochastic staticization control problems (including minimization problems with smooth, convex, coercive payoffs) driven by diffusion dynamics with constant diffusion coefficient is considered. A fundamental solution form is obtained where the same solution can be used for a limited variety of terminal costs without re-solution of the problem. One may convert this fundamental solution form from a stochastic control problem form to a deterministic control problem form. This yields an equivalence between certain second-order (in space) Hamilton-Jacobi partial differential equations (HJ PDEs) and associated first-order HJ PDEs. This reformulation has substantial numerical implications.
Paper VI114-02.9 
PDF · Video · Convergence of Stochastic Vector Quantization and Learning Vector Quantization with Bregman Divergences (I)

Mavridis, ChristosUniversity of Maryland
Baras, John S.Univ. of Maryland
Keywords: Machine learning, Bayesian methods, Stochastic system identification
Abstract: Stochastic vector quantization methods have been extensively studied in supervised and unsupervised learning problems as online, data-driven, interpretable, robust, and fast to train and evaluate algorithms. Being prototype-based methods, they depend on a dissimilarity measure, which is both necessary and sufficient to belong to the family of Bregman divergences, if the mean value is used as the representative of the cluster. In this work, we investigate the convergence properties of stochastic vector quantization (VQ) and its supervised counterpart, Learning Vector Quantization (LVQ), using Bregman divergences. We employ the theory of stochastic approximation to study the conditions on the initialization and the Bregman divergence generating functions, under which, the algorithms converge to desired configurations. These results formally support the use of Bregman divergences, such as the Kullback-Leibler divergence, in vector quantization algorithms.
Paper VI114-02.10 
PDF · Video · Strong Solution Existence for a Class of Degenerate Stochastic Differential Equations (I)

McEneaney, WilliamUniv of California, San Diego
Kaise, HidehiroNagoya University
Dower, Peter M.University of Melbourne
Zhao, RuobingUCSD
Keywords: Stochastic control and game theory
Abstract: Existence and uniqueness results for stochastic differential equations (SDEs) under exceptionally weak conditions are well known in the case where the diffusion coefficient is nondegenerate. Here, existence of a strong solution is obtained in the case of degenerate SDEs in a class that is motivated by diffusion representations for solution of Schrodinger initial value problems. In such examples, the dimension of the range of the diffusion coefficient is exactly half that of the state. In addition to the degeneracy, two types of discontinuities and singularities in the drift are allowed, where these are motivated by the structure of the Coulomb potential. The first type consists of discontinuities that may occur on a possibly high-dimensional manifold (up to codimension one). The second consists of singularities that may occur on a lower-dimensional manifold (up to codimension two).
Paper VI114-02.11 
PDF · Video · Security of Control Systems with Erroneous Observations (I)

Kim, JaewonTexas A&M University
Kumar, P. R.Texas A&M University
Keywords: Secure networked control systems
Abstract: We address the problem of security of stochastic control systems when observation measurements used to close the control loop may be erroneous, due to a malicious adversary who has intercepted the associated sensors or the communication network. We show how the method of dynamic watermarking can be employed to secure such a system. This is a method of defense based on stochastic considerations, relying on the inability of the attacker to separate the ambient noise present in the system from a deliberatively superimposed random watermark. We present the results of experiments against several attacks, and show the capability of this method to detect attacks in all the tested cases. The experiments are conducted on a prototypical process control system consisting of two coupled water tanks.

Keywords: Security, Malicious Sensors, Cyber Physical Systems, Dynamic Watermarking.

Paper VI114-02.12 
PDF · Video · Machine Learning Model to Characterize Seizure Development in Traumatic Brain Injury Patients (I)

La Rocca, MariannaUniversity of Southern California
Garner, RachaelUniversity of Southern California
Duncan, DominiqueUniversity of Southern California
Keywords: Machine learning, Stochastic system identification, Stochastic adaptive control
Abstract: Traumatic brain injury (TBI) occurs in 69 million people annually and many patients go on to develop disabling disorders such as post-traumatic epilepsy (PTE). This work focuses on data modeling and analysis for TBI patients who develop seizures. We investigated and analyzed MRI scans using voxel-based morphometry (VBM) to characterize gray level intensity differences between TBI patients who developed seizures and TBI patients who have not developed seizures. We used MRI scans from the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy, which aims to identify epileptogenic biomarkers through an international project involving multiple species, modalities, and research institutions. Using the VBM approach, statistically significant voxel changes were identified between the two clinical groups in different brain regions. Stochastic modeling and statistical analysis of the data in terms of interesting, confounding factors (age and total intracranial volume) and residual variability applied to each voxel independently, are presented. Statistical inference is used to test hypotheses that are expressed as functions of the General Linear Model estimated regression parameters. In addition, we used significant voxels to train a Neural Network (NN) classifier and evaluate the informative power of the proposed approach. The NN was able to distinguish the two clinical groups with an Area Under the receiver operating characteristics Curve of 62%.
VI114-03
Stochastic Control and Game Theory Regular Session
Chair: Huang, MinyiCarleton University
Co-Chair: Cetinkaya, AhmetNational Institute of Informatics
Paper VI114-03.1 
PDF · Video · Fixed Points of Set-Based Bellman Operator

Li, Sarah H.Q.University of Washington
Adjé, AssaléUniversité De Perpignan Via-Domitia
Garoche, Pierre-LoicONERA Toulouse
Acikmese, BehcetUniversity of Washington
Keywords: Stochastic control and game theory
Abstract: Motivated by uncertain parameters encountered in Markov decision processes (MDPs), we study the effect of parameter uncertainty on Bellman operator-based methods. Specifically, we consider a family of MDPs where the cost parameters are from a given compact set. We then define a Bellman operator acting on an input set of value functions to produce a new set of value functions as the output under all possible variations in the cost parameters. Finally we prove the existence of a fixed point of this set-based Bellman operator by proving that it is a contractive operator on a complete metric space.
Paper VI114-03.2 
PDF · Video · Mean Field Stackelberg Games: State Feedback Equilibrium

Huang, MinyiCarleton University
Yang, XuweiCarleton University
Keywords: Stochastic control and game theory, Complex system management
Abstract: We study mean field Stackelberg games between a major player (the leader) and a large population of minor players (the followers). By treating the mean field as part of the dynamics of the major player and a representative minor player, we Markovianize the decision problems and employ dynamic programming to determine the equilibrium strategy in a state feedback form. We show that for linear quadratic (LQ) models, the feedback equilibrium strategy is time consistent. We further give the explicit solution in a discrete-time LQ model.
Paper VI114-03.3 
PDF · Video · Mean-Field Type Quantum Filter for a Quantum Ising Type System

Ohki, KentaroKyoto University
Keywords: Stochastic control and game theory, Filtering and smoothing, Estimation and filtering
Abstract: Mean-field games or mean-field type control problems are one of distributed control schemes that reduce the computational burden. In this paper, a quantum version of mean-field game settings is developed and the mean-field type quantum filter is derived for quantum Ising models.
Paper VI114-03.4 
PDF · Video · Mixed H2/Hoo State-Feedback Control of Continuous-Time Markov Jump Systems with Partial Observations of the Markov Chain

Marcorin de Oliveira, AndréUNIFESP
Costa, Oswaldo Luiz V.Univ. of Sao Paulo
Keywords: Stochastic control and game theory, Optimal control of hybrid systems, Stochastic hybrid systems
Abstract: We study the mixed H2/Hoo state-feedback control of continuous-time Markov jump linear systems considering that the Markov chain is not observable, with the only information available to the controller coming from the output of a fault-detection and isolation device. We present sufficient design conditions given in terms of linear matrix inequalities so that the closed-loop system is stable and its H2 and Hoo norms are bounded. We present an illustrative example in which we investigate the behavior of the proposed algorithm.
Paper VI114-03.5 
PDF · Video · Gain-Scheduled Hinf Controller Synthesis for LPV Systems Subject to Multiplicative Noise

Levano, ElmerFederal University of Technology -- Paraná
Oliveira, Ricardo C. L. F.University of Campinas
Vargas, Alessandro N.Univ. Tec. Federal Do Parana, UTFPR
Keywords: Stochastic control and game theory, Stochastic adaptive control, Adaptive gain scheduling autotuning control and switching control
Abstract: This paper proposes LMI conditions to design parameter-dependent (i.e. gain-scheduled) state-feedback controllers that ensure closed-loop stability with guaranteed Hinf performance for both continuous and discrete-time LPV systems with state multiplicative noise. The state-space matrices and the multiplicative noise matrix are considered polytopic and independent. The time-varying parameters can be considered time-invariant, arbitrarily fast or with bounded rates of variation. The advantages of the proposed technique are illustrated by numerical examples borrowed from the literature.
Paper VI114-03.6 
PDF · Video · Conditions of Almost Sure Boundedness and Practical Asymptotic Stability of Continuous-Time Stochastic Systems

Hoshino, KentaKyoto University
Nishimura, YukiKagoshima University
Keywords: Stochastic control and game theory, Synthesis of stochastic systems
Abstract: This paper investigates the boundedness conditions of solutions of stochastic differential equations in the almost sure sense. Boundedness is one of the most fundamental properties in a lot of control problems. In general, it is hard to investigate almost sure boundedness of solutions of stochastic differential equations, unlike deterministic systems. However, for a class of systems, the almost sure boundedness can be investigated. This paper deals with conditions for the almost sure boundedness of stochastic systems, which is based on boundary properties of one-dimensional diffusion processes. Moreover, based on the boundedness, we show the characterization of a kind of practical asymptotic stability of one-dimensional stochastic systems in the almost sure sense. The presented results are validated through a numerical example.
Paper VI114-03.7 
PDF · Video · An Impossibility Result Concerning Bounded Average-Moment Control of Linear Stochastic Systems

Cetinkaya, AhmetNational Institute of Informatics
Kishida, MasakoNational Institute of Informatics
Keywords: Stochastic control and game theory, Synthesis of stochastic systems, Control over networks
Abstract: It is known that strictly unstable linear systems that are subject to nonvanishing additive stochastic noise with unbounded supports cannot be stabilized by using deterministically bounded control inputs. In this paper, we explore similar impossibility results for scenarios where the expected value of the squared control input norm is subject to constraints and the support of the noise distribution is not necessarily unbounded. Specifically, we consider the stabilization problem with control policies that have bounded time-averaged second moments. We obtain values of such average second moment bounds, below which stabilization is not possible and the second moment of the state diverges regardless of the choice of the control policy and the initial state distribution. The results are illustrated with a numerical example.
Paper VI114-03.8 
PDF · Video · Stability Analysis for Linear Systems with Time-Varying and Time-Invariant Stochastic Parameters

Ito, YujiToyota Central R&d Labs., Inc
Fujimoto, KenjiKyoto University
Keywords: Synthesis of stochastic systems, Stochastic control and game theory
Abstract: This paper presents a method to guarantee stability of linear stochastic systems. The systems include both time-varying and time-invariant unknown stochastic parameters simultaneously. For analyzing the stability, such a system is represented by an expanded system that contains only the time-invariant stochastic parameter. This expansion excludes the time-varying parameter from the system, which simplifies the stability analysis. Existing methods on robust stability theory can be thus employed to ensure stability of the expanded system. Guaranteeing stability of the expanded system is a necessary and/or sufficient condition for that of the original system. Consequently, the stability of the original system is evaluated by using linear matrix inequalities.
VI114-04
Stochastic Systems Estimation and Filtering Regular Session
Chair: King, RudibertTechnische Universitaet Berlin
Co-Chair: Hanebeck, UweKarlsruhe Institute of Technology (KIT)
Paper VI114-04.1 
PDF · Video · A Fundamental Bound on Performance of Non-Intrusive Load Monitoring Algorithms with Application to Smart-Meter Privacy

Farokhi, FarhadThe University of Melbourne
Keywords: Estimation and filtering
Abstract: We consider non-intrusive load monitoring by a sophisticated adversary that knows the load profiles of the appliances and wants to determine their start-finish times based on smart-meter readings. We prove that the expected estimation error of non-intrusive load monitoring algorithms is lower bounded by the trace of the inverse of the cross-correlation matrix between the derivatives of the load profiles of the appliances. This is an interesting observation illustrating that the derivatives of the load profiles are more important than the profiles themselves for non-intrusive load monitoring (i.e., small rapidly-changing loads are easier to identify than large, yet slowly-varying ones). This fundamental bound on the performance of non-intrusive load monitoring adversaries is used to develop privacy-preserving policies. Particularly, we devise a load-scheduling policy by maximizing the lower bound on the expected estimation error of non-intrusive load monitoring algorithms.
Paper VI114-04.2 
PDF · Video · On the Resilience of a Class of Correntropy-Based State Estimators

Kircher, AlexandreLaboratoire Ampère, Ecole Centrale De Lyon
Bako, LaurentEcole Centrale De Lyon
Blanco, EricAMPERE Laboratory
Benallouch, MohamedLouis Pasteur Univ
Keywords: Estimation and filtering
Abstract: This paper deals with the analysis of a class of offline state estimators for LTI discrete-time systems in the presence of an arbitrary measurement noise which can potentially take any value. The considered class of estimators is defined as the solution of an optimization problem involving a performance function which can be interpreted as a generalization of cost functions used in the Maximum Correntropy Criterion. The conclusion of the analysis is that if the system is observable enough, then the considered class of estimators is resilient, which means that the obtained estimation error is independent from the highest values of the measurement noise. In the case of systems with a bounded process noise, the considered class of estimators provides a bounded estimation error under the appropriate conditions despite not being designed for this scenario.
Paper VI114-04.3 
PDF · Video · Identifying Trending Coefficients with an Ensemble Kalman Filter - a Demonstration on a Force Model for Milling (I)

Schwenzer, MaxRWTH Aachen University
Visconti, GiuseppeRWTH Aachen University
Ay, MuzafferRWTH Aachen University
Bergs, ThomasRWTH Aachen University
Herty, MichaelRWTH Aachen
Abel, DirkRWTH-Aachen University
Keywords: Estimation and filtering
Abstract: This paper extends the ensemble Kalman filter (EnKF) for inverse problems to identify trending model coefficients. This is done by repeatedly inflating the ensemble while maintaining the mean of the particles. As a benchmark serves a classic EnKF and a recursive least squares (RLS) on the example of identifying a force model in milling, which changes due to the progression of tool wear. For a proper comparison, the true values are simulated and augmented with white Gaussian noise. The results demonstrate the feasibility of the approach for dynamic identification while still achieving good accuracy in the static case. Further, the inflated EnKF shows a remarkably insensitivity on the starting set but a less smooth convergence compared to the classic EnKF.
Paper VI114-04.4 
PDF · Video · Extended State Based Kalman Filter for Uncertain Systems with Bias (I)

Zhang, XiaochengAcademy of Mathematics and Systems Science, Chinese Academy of S
Xue, WenchaoChinese Academy of Sciences, Beijing 100190,
Fang, HaitaoAcademyofMathematicsandSystemsScience, ChineseAcademyofSciences
Li, ShihuaSoutheast University
Yang, JunSoutheast University
Keywords: Estimation and filtering
Abstract: This paper addresses the state estimation for a class of stochastic systems with both uncertain dynamics and measurement bias. By using the idea of uncertainty/disturbance estimation, an extended state based Kalman filter algorithm is developed to estimate the original state, the uncertain dynamics and the measurement bias. Furthermore, a necessary and sufficient condition for the observability of augmented system is presented. Also, the stability of the proposed algorithm is analyzed. It is shown that the proposed filter can achieve unbiased estimation of measurement bias, such that the influence of measurement bias is eliminated. Finally, a simulation study is provided to illustrate the effectiveness of proposed method.
Paper VI114-04.5 
PDF · Video · Kernel-Based Learning of Orthogonal Functions

Scampicchio, AnnaUniversity of Padova
Pillonetto, GianluigiUniv of Padova
Bisiacco, MauroUniv of Padova
Keywords: Estimation and filtering, Bayesian methods, Particle filtering/Monte Carlo methods
Abstract: The paper deals with the reconstruction of functions from sparse and noisy data in suitable intersections of Hilbert spaces that account for orthogonality constraints. Such problem is becoming more and more relevant in several areas like imaging, dictionary learning, compressed sensing. We propose a new approach where it is interpreted as a particular kernel-based multi-task learning problem, with regularization formulated in a reproducing kernel Hilbert space. Special penalty terms are then designed to induce orthogonality. We show that the problem can be given a Bayesian interpretation. This then permits to overcome nonconvexity through a novel Markov chain Monte Carlo scheme able to recover the posterior of the unknown functions and also to understand from data if the orthogonal constraints really hold.
Paper VI114-04.6 
PDF · Video · State Estimation of a Benchmark Two-Tank Problem: A Carleman Linearization Approach

Bhatt, DhruviSardar Vallabhbhai National Institute of Technology
Sharma, Shambhu N.National Institute of Technology, Surat, Gujarat
Keywords: Estimation and filtering, Continuous time system estimation
Abstract: The two-tank problem is often considered as a challenging benchmark problem of process control, owing to its non-linear nature and non-minimum phase behavior. Non-linearity arises due to the dependence of non-linear outlet flow associated with the tank level. Hence, it is significant to investigate the dynamics of the system. This paper utilizes a novel idea of transforming the non-linear Stochastic Differential Equations (SDEs) to an appropriate form of the SDEs that preserves non-linear effects. In this paper, first, the Carleman linearization technique is explored to arrive at the bi-linearized two-tank SDEs. Then, we utilize the Fokker-Planck equation for the estimation of the bi-linearized two-tank problem. The theoretical results corroborated with numerical simulations highlight the effectiveness of the proposed Carleman linearization-based estimation method in contrast to the benchmark EKF-prediction method, i.e. without observation.
Paper VI114-04.7 
PDF · Video · Pose Observation for Second Order Pose Kinematics

Ng, YonhonAustralian National University
van Goor, PieterAustralian National University
Mahony, RobertAustralian National University
Keywords: Estimation and filtering, Continuous time system estimation
Abstract: This paper proposes an equivariant observer for second order pose estimation of a rigid body. The observer exploits the second order kinematic model and its symmetry group. The observer uses conventional sensors and simple computations that allow it to be run on resource-constrained devices. The observer design is based on the lifted kinematics and we prove its asymptotic convergence property. The performance of the observer is demonstrated in simulation.
Paper VI114-04.8 
PDF · Video · State Estimation with Event-Based Inputs Using Stochastic Triggers

Noack, BenjaminKarlsruhe Institute of Technology (KIT)
Funk, ChristopherKarlsruhe Institute of Technology (KIT)
Radtke, SusanneKIT – the Research University in the Helmholtz Association
Hanebeck, UweKarlsruhe Institute of Technology (KIT)
Keywords: Estimation and filtering, Distributed control and estimation, Sensor networks
Abstract: Event-based communication and state estimation offer the potential to improve resource utilization in networked sensor and control systems significantly. Sensor nodes can trigger transmissions when data are deemed useful for the remote estimation units. To improve the estimation performance, the remote estimator can exploit the implicit information conveyed by the event trigger even if no transmission is triggered. The implicit information is typically incorporated into the measurement update of a remote Kalman filter. In this paper, event-triggered transmissions of input data are investigated that enter the prediction step of the remote estimator. By employing a stochastic trigger, the implicit input information remains Gaussian and can easily be incorporated into the remote Kalman filter. The proposed event-based scheme is evaluated in remote tracking scenarios, where system inputs are transmitted aperiodically.
Paper VI114-04.9 
PDF · Video · Boundedness of the Kitanidis Filter for Optimal Robust State Estimation

Zhang, QinghuaINRIA
Keywords: Estimation and filtering, Fault detection and diagnosis
Abstract: The Kitanidis filter is a natural extension of the Kalman filter to systems subject to arbitrary disturbances or unknown inputs. Though the optimality of the Kitanidis filter was founded for general time varying systems more than 30 years ago, its stability analysis is still limited to time invariant systems, to the author's knowledge. In the framework of general time varying systems, this paper establishes upper bounds for the error covariance of the Kitanidis filter and for all the auxiliary variables involved in the filter.
Paper VI114-04.10 
PDF · Video · A Comparative Study of Kalman-Like Filters for State Estimation of Turning Aircraft in Presence of Glint Noise

Kulikov, Gennady Yu.Instituto Superior Tecnico, Universidade De Lisboa
Kulikova, Maria V.Instituto Superior Técnico, Universidade De Lisboa
Keywords: Estimation and filtering, Filtering and smoothing
Abstract: This paper continues the study started by Kulikov and Kulikova on state estimation accuracies of various Kalman-like filtering techniques in target tracking scenarios with non-Gaussian noise in 2018. The cited authors examined a number of methods, which are grounded in the minimum-variance or maximum-correntropy criteria and cover extended-, cubature- and unscented-type Kalman filters, in the well-known turning aircraft scenario with impulsive (shot) noise or mixed-Gaussian one. Despite the success of the maximum-correntropy-based filtering methods reported on estimation of linear discrete-time stochastic systems in literature, those case studies expose the superiority of the cubature and unscented Kalman filters towards various extended Kalman methods designed in the minimum-variance sense or grounded in the maximum-correntropy criterion within the mentioned target tracking scenarios. Here, we extend that examination to the turning aircraft scenario with glint noise, which is simulated by a sum of two zero-mean Gaussian variables with difference covariances. In particular, our study reveals a valued potential of the maximum-correntropy-based accurate continuous-discrete extended Kalman filters devised by the above authors in this glint noise state estimation environment.
Paper VI114-04.11 
PDF · Video · Modified Extended Kalman Filtering for Nonlinear Stochastic Differential Algebraic Systems

Bhase, Swapnil SHomi Bhabha National Institute, Mumbai
Bhushan, ManiIndian Institute of Technology Bombay
Kadu, Sachin C.Bhaba Atomic Research Centre (BARC), Mumbai
Mukhopadhyay, SulekhaBhabha Atomic Research Center
Keywords: Estimation and filtering, Filtering and smoothing
Abstract: The extended Kalman filter (EKF) is one of the most widely used nonlinear filtering technique for a system of differential algebraic equations (DAEs). In this work we propose an alternate EKF approach for state estimation of nonlinear DAE systems that addresses shortcomings of the EKF approaches available in literature (Becerra et al., 2001; Mandela et al. 2010). The proposed approach is based on the idea that since the algebraic equations are assumed to be exact, the error covariance matrix of only the differential states needs to be directly propagated during the prediction step. The error covariance matrix for algebraic states and cross covariance matrix between the errors in differential and algebraic states, which are required to incorporate effect of prior algebraic state estimates on the update step, can be computed from the differential state error covariance matrix alone using the linearized algebraic equations. The update step of the proposed work also follows a similar philosophy and ensures that the covariance update is not approximate. The efficacy of the proposed EKF approach is evaluated using benchmark case studies of a Galvanostatic charge process and a drum boiler.
Paper VI114-04.12 
PDF · Video · Hyperspherical Unscented Particle Filter for Nonlinear Orientation Estimation

Li, KailaiKarlsruhe Institute of Technology (KIT)
Pfaff, FlorianKarlsruhe Institute of Technology
Hanebeck, UweKarlsruhe Institute of Technology (KIT)
Keywords: Estimation and filtering, Filtering and smoothing
Abstract: We propose a novel quaternion particle filter for nonlinear SO(3) estimation. For importance sampling, the proposal distribution is designed to incorporate newly observed evidence. For that, the unscented Kalman filtering is performed particle-wise on the tangent plane of the unit quaternion manifold via gnomonic projection/retraction based on hyperspherical geometry. As prior particles are driven towards high-likelihood regions on the manifold, computational efficiency of quaternion particle filtering is significantly improved. The resulting hyperspherical unscented particle filter (HUPF) is evaluated for nonlinear orientation estimation in simulations. Results show that it gives superior tracking performance compared with the conventional particle filter and other existing quaternion filtering schemes relying on parametric modeling.
Paper VI114-04.13 
PDF · Video · The Spherical Grid Filter for Nonlinear Estimation on the Unit Sphere

Pfaff, FlorianKarlsruhe Institute of Technology (KIT)
Li, KailaiKarlsruhe Institute of Technology (KIT)
Hanebeck, UweKarlsruhe Institute of Technology (KIT)
Keywords: Estimation and filtering, Filtering and smoothing, Bayesian methods
Abstract: Filters for the unit sphere have to consider its inherent periodic nature. Since the unit sphere is a domain of finite size, suitable grids covering the manifold can be provided. We explain the considerations for the grid generation and provide efficient ways to implement the prediction and update steps of a novel grid filter for this manifold. The filter supports nonlinear system and measurement models in the form of transition densities and likelihoods. In the evaluation, the proposed filter achieves a higher estimation accuracy than competing approaches.
Paper VI114-04.14 
PDF · Video · The J-Orthogonal Square-Root Euler-Maruyama-Based Unscented Kalman Filter for Nonlinear Stochastic Systems

Kulikov, Gennady Yu.Instituto Superior Tecnico, Universidade De Lisboa
Kulikova, Maria V.Instituto Superior Técnico, Universidade De Lisboa
Keywords: Estimation and filtering, Filtering and smoothing, Continuous time system estimation
Abstract: This paper addresses the issue of square-rooting in the Unscented Kalman Filtering (UKF) methods. Since their discovery the UKF is considered to be among the most valued state estimation algorithms because of its outstanding performance in numerous real-world applications. However, the main shortcoming of such a technique is the need for the Cholesky decomposition of predicted and filtering covariances derived in all time and measurement update steps. Such a factorization is time-consuming and highly sensitive to round-off and other errors committed in the course of calculation, which can result in losing the covariance's positivity and, hence, in failing the Cholesky decomposition. The latter problem is usually overcome via square-root filtering implementations, which propagate not the covariance itself but only its square root (Cholesky factor). Unfortunately, negative weights arising in applications of the UKF schemes to large stochastic systems preclude from designing conventional square-root UKF methods. So, we resolve it with a hyperbolic QR factorization used for yielding J-orthogonal square roots. Our novel square-root filter is grounded in the Euler-Maruyama discretization of order 0.5. It is justified theoretically and examined and compared numerically to the conventional (non-square-root) UKF in an aircraft's coordinated turn scenario with ill-conditioned measurements.
Paper VI114-04.15 
PDF · Video · Estimation of Physical Parameters Using a New Discrete-Time Derivative Algorithm

Tebaldi, DavideUniv. of Modena and Reggio Emilia
Morselli, RiccardoDANA
Zanasi, RobertoUniv of Modena and Reggio Emilia
Keywords: Estimation and filtering, Nonlinear system identification, Mechanical and aerospace estimation
Abstract: The paper presents a parameters estimation procedure for physical systems modeled using the POG (Power-Oriented Graphs) technique. The coefficients defining the constitutive relation for both static and dynamic physical elements within the system can be estimated, as well as the coefficients describing energy conversions taking place either within the same energetic domain or between two different energetic domains. The evolution of the state vector over time is supposed to be known, whereas its first derivative is supposed to be unknown and is obtained by using a new algorithm for computing the discrete-time derivative of a sampled signal, which is effective even in presence of disturbances affecting the signal samples. As long as the unknown parameters appear linearly within the system differential equations, the system is allowed to exhibit any nonlinear function of the state vector and its first derivative. The procedure is finally applied to two different case studies: a linear one and a nonlinear one.
Paper VI114-04.16 
PDF · Video · Optimal Transport Based Filtering with Nonlinear State Equality Constraints

Das, NiladriTexas A&M University
Bhattacharya, RaktimTexas A&M
Keywords: Estimation and filtering, Particle filtering/Monte Carlo methods, Filtering and smoothing
Abstract: In this work we propose a framework to address the issue of state dependent nonlinear equality-constrained state estimation using Bayesian filtering. This framework is constructed specifically for a linear approximation of Bayesian filtering that uses the theory of Optimal Transport. As a part of this framework, we present three traditionally used nonlinear equality constraint-preserving algorithms coupled with the Optimal Transport based filter: the equality-constrained Optimal Transport filter, the projected Optimal Transport filter, and the measurement-augmented Optimal Transport filter. In cases where the nonlinear equality- constraints represent an arbitrary convex manifold, we show that the re-sampling step of Optimal Transport filter, can generate initial samples for filtering, from any probability distribution function defined on this manifold. We show numerical results using our proposed framework.
Paper VI114-04.17 
PDF · Video · A Comparative Investigation of Information Loss Due to Variable Quantization on Parameter Estimation of Compound Distribution

Seifullaev, RuslanUppsala University
Knorn, SteffiUppsala University
Ahlen, AndersUppsala University
Keywords: Estimation and filtering, Quantized systems, Control and estimation with data loss
Abstract: In this paper we study the problem of how quantization may affect the maximum likelihood estimation of the parameters of a probability density function representing a compound distribution. We consider and compare three different approaches to design a variable quantizer allowing to guarantee a predefined loss of Fisher information which is used as a measure of the information loss due to quantization. We also propose the approximations which characterize the asymptotic behavior of the loss allowing a significant reduction of the computational complexity.
Paper VI114-04.18 
PDF · Video · Probabilistic Bounds on Vehicle Trajectory Prediction Using Scenario Approach

Shen, XunTokyo University of Agriculture and Technology
Zhang, XingguoTokyo University of Agriculture and Technology
Raksincharoensak, PongsathornTokyo University of Agriculture and Technology
Keywords: Estimation and filtering, Randomized methods, Particle filtering/Monte Carlo methods
Abstract: The automotive industry concerns about improving road safety. One of the major challenges is to assess road risk and react accordingly in order to avoid accidents. This requires predicting the evolution of the surrounding vehicle trajectories. However, the prediction involves uncertainties from driver operations and ground situations. It is critical to obtain the vehicle trajectory prediction with probabilistic-guarantee bounds. This contribution paper proposes a novel approach to obtain probabilistic ellipsoidal bounds for vehicle trajectory prediction. The vehicle dynamics model adopts a classical bicycle model. The uncertainty of the future trajectory is from the driver's intend and road condition which can be simplified by setting some parameters of the vehicle dynamics model as a stochastic model. Then, a stochastic optimization problem is formulated to obtain the probabilistic ellipsoidal bounds on the future vehicle trajectories. The proposed approach is validated in a numerical simulation which shows the relationship between the computation complexity and the conservatism of the probabilistic ellipsoidal bounds. The proposed method can be generally used for a physics-based motion method, maneuver-based motion method, and interaction-aware motion method by defining the probability distribution of uncertain variables differently.
Paper VI114-04.19 
PDF · Video · Real-Time Estimation of Parameter Maps

Gentsch, MaikTechnische Universität Berlin
King, RudibertTechnische Universitaet Berlin
Keywords: Estimation and filtering, Recursive identification, Adaptive observer design
Abstract: System parameters might have a distinct operating point dependency that is unknown. Nonlinear state observers or Kalman Filters can be applied to estimate such parameters in real-time, revealing the unknown parameter value in the vicinity of the current operating point. Commonly, these methods are prone to forget the revealed dependence continuously when a different operating point is approached. This paper provides a procedure to preserve past estimates and reveal the hidden parameter map during operation of the system. Parameter dependencies are approximated via adjustable interpolants. In particular, ready-to-use formulae for piecewise linear and cubic Hermite interpolants are provided. An existing approach as well as a newly derived approach to embed these interpolants within an Unscented Kalman Filter are presented and discussed. While the first approach utilizes the parameter map estimation directly within the Kalman Filter scheme, the new approach expands the Kalman Filter steps by a recursive map adaption scheme and is thereby far less computationally expensive. Both methods are compared and validated via numeric simulations, where a superior performance is achieved compared to the standard parameter estimation within the Kalman Filter approach.
Paper VI114-04.20 
PDF · Video · On the Stability of Kalman Filter with Random Coefficients

Gan, DieAcademy of Mathematics and Systems Science
Liu, ZhixinAcademy of Mathematics and Systems Sciences
Keywords: Estimation and filtering, Stochastic system identification
Abstract: Inspired by the random packet dropout problem widely existing in the networked control systems, we investigate the stability of Kalman filter with random coefficients. We present an excitation condition about the regression vectors to establish the Lp-stability and Lp-exponential stability of random Riccati equation. Furthermore, we prove the stability of the error equations of Kalman filter under the excitation condition and some conditions on the system matrix and noises, without relying on any stationarity or independence assumptions about the regressors.
Paper VI114-04.21 
PDF · Video · State Estimation in the Presence of Intermittent Actuator Faults

Strandt, AliaMarquette University
Schneider, SusanMarquette University
Yaz, EdwinMarquette University
Keywords: Estimation and filtering, Stochastic system identification, Bayesian methods
Abstract: The problem of intermittent, random actuator faults is important in many applications, such as in networked systems, in which there may be intermittent losses of communication between the actuators and the plant. However, state estimation of such systems is rarely addressed, with the majority of the work focusing on fault-tolerant control. In this work, the Kalman filter is modified for state estimation of systems with intermittent actuator faults when the fault rate is known. The proposed estimator is then extended to the case when the actuator fault rate is unknown using the multiple model estimation algorithm. In addition, a sketch of a proof of convergence for this technique is provided. Several simulations involving a DC motor that experiences random actuator faults demonstrate the effectiveness of the proposed techniques.
Paper VI114-04.22 
PDF · Video · On the Stratonovich Approach for a Satellite Dynamics

Hirpara, Ravish H.S.V. National Institute of Technology, Surat
Sharma, Shambhu N.National Institute of Technology, Surat, Gujarat
Keywords: Estimation and filtering, Stochastic system identification, Nonlinear system identification
Abstract: In contrast to a vector non-linear stochastic differential equation (SDE) describing the satellite dynamics under the ‘fluctuating aerodynamic torque’, this paper analyses a second-order fluctuation equation for the radial perturbation about the given orbit. The second-order fluctuation equation for the radial perturbation has found its application for the satellite orbital stability. After accomplishing a phase space formulation, we arrive at the two-dimensional SDE. Most notably, the inaccurate choice of stochastic integral describing the satellite stochastic dynamics will have influence on their estimation, stability and control. For this reason, we develop a noise equation of the satellite dynamics in the Stratonovich setting. The satellite dynamics in the Stratonovich sense can be expressed equivalently in the Itô setting by accounting additional correction terms in the system non-linearity term of the SDE. This paper develops the estimation theory of satellite dynamics via the Stratonovich calculus. The analytic findings are useful to the trajectory estimation of the orbiting satellite under the influence of atmospheric dust perturbations, where the observations are not available.
VI114-05
Stochastic System Identification Regular Session
Chair: Ikeda, KenjiTokushima University
Co-Chair: Straka, OndrejUniversity of West Bohemia
Paper VI114-05.1 
PDF · Video · Estimation of Parameters of Gaussian Sum Distributed Noises in State-Space Models

Dunik, JindrichUniversity of West Bohemia
Kost, OliverUniversity of West Bohemia
Straka, OndrejUniversity of West Bohemia
Keywords: Stochastic system identification, Estimation and filtering
Abstract: The paper deals with the estimation of noise parameters of a linear time-varying system. In particular, the stress is laid on the state-space models, where the state and measurement noises are described by the Gaussian sum probability density functions. The recently introduced measurement difference method for the estimation of higher-order moments of the state and measurement noises is revised and, subsequently, extended for estimation of the parameters of the noise Gaussian sum densities with a special focus on the densities with two-components. The theoretical results are discussed and illustrated in a numerical example.
Paper VI114-05.2 
PDF · Video · Identification of Dynamic Textures Using Dynamic Mode Decomposition

Previtali, DavideUniversity of Bergamo
Valceschini, NicholasUniversity of Bergamo
Mazzoleni, MirkoUniversity of Bergamo
Previdi, FabioUniversita' Degli Studi Di Bergamo
Keywords: Stochastic system identification, Estimation and filtering
Abstract: Dynamic Textures (DTs) are image sequences of moving scenes that present stationary properties in time. In this paper, we apply Dynamic Mode Decomposition (DMD) and Dynamic Mode Decomposition with Control (DMDc) to identify a parametric model of dynamic textures. The identification results are compared with a benchmark method from the dynamic texture literature, both from a mathematical and from a computational complexity point of view. Extensive simulations are carried out to assess the performance of the proposed algorithms with regards to synthesis and denoising purposes, with different types of dynamic textures. Results show that DMD and DMDc present lower error, lower residual noise and lower variance compared to the benchmark approach.
Paper VI114-05.3 
PDF · Video · Subspace Identification Algorithm for Stochastic Systems Equipped with Zeros Close to Unit Circle

Tanaka, HideyukiHiroshima University
Ikeda, KenjiTokushima University
Keywords: Stochastic system identification, Realization theory, Subspace methods
Abstract: In identifying a stochastic system possessing zeros close to the unit circle, the effect of the initial state appears in the estimates. This paper derives a stochastic subspace identification algorithm for such a system. A new stochastic realization algorithm is developed based on the covariance matrices of the state and the white-noise input, by taking the initial state and positive realness into account. A subspace identification algorithm is obtained by applying the realization algorithm to a finite string of data. Numerical simulation results show that the proposed algorithm provides favorable results compared with the conventional ones.
Paper VI114-05.4 
PDF · Video · A Computation Approach to Chance Constrained Optimization of Boundary-Value Parabolic Partial Differential Equation Systems

Nida, Kibru TekaTechnical University of Ilmenau
Geletu, AbebeIlmenau University of Technology
Li, PuTechnische Universität Ilmenau
Keywords: Stochastic system identification, Synthesis of stochastic systems, Distributed control and estimation
Abstract: This work studies chance constrained optimization of boundary-value parabolic partial differential equations (CCPDE) with random data, where the PDE model is treated as equality constraint and chance constraints are imposed on inequality constraints involving state variables. Since such a CCPDE problem is generally non-smooth, non-convex and difficult to solve directly, we use our recently proposed smoothing approximation method to solve the problem. As a result, the probability function of the chance constraints is approximated in two different ways by a family of differentiable functions. This leads to two smooth parametric optimization problems IA_tau and OA_tau, where the feasible sets of IA_tau are always subsets (inner approximation) and the feasible sets of OA_tau always supersets (outer approximation). The feasible sets of IA_tau (resp. OA_tau) converge asymptotically to the feasible set of the CCPDE. Moreover, any limit point of a sequence of optimal solutions of IA_tau (resp. OA_tau) is a stationary point of CCPDE. The viability of the approximation approach is numerically demonstrated by optimal thermal cancer treatment as a case study.
Paper VI114-05.5 
PDF · Video · Synthesis of Stochastic Systems with Partial Information Via Control Barrier Functions

Jahanshahi, NiloofarLudwig Maximilian University of Munich
Jagtap, PushpakTechnical University of Munich, Munich, Germany
Zamani, MajidUniversity of Colorado Boulder
Keywords: Synthesis of stochastic systems, Identification for control, Continuous time system estimation
Abstract: Synthesis of controllers for stochastic control systems ensuring safety constraints has gained considerable attention in the last few years. In this paper, we consider the problem of synthesizing controllers for partially observed stochastic control systems to ensure finite-time safety. Given an estimator with a probabilistic guarantee on the accuracy of the estimations, we provide an approach to compute a controller providing a lower bound on the probability that the trajectories of the stochastic control system remain safe over a finite time-horizon. To obtain such controllers, we utilize a notion of control barrier functions. We also provide an approach to compute a probability bound on estimator accuracy by using a notion of so-called stochastic simulation function. The proposed result is illustrated on a case study.
VI115
Systems and Signals - Networked Systems
VI115-01 Cooperative Control of Unmanned Aerial Vehicles: Reliability and Autonomy   Invited Session, 9 papers
VI115-02 Resilience Analysis, Security Countermeasures and Privacy Protection in Distributed State Estimation   Invited Session, 4 papers
VI115-03 Resilient and Networked Control of Complex Cyber-Physical Systems   Invited Session, 6 papers
VI115-04 Resilient Large-Scale Networks: Spreading and Bifurcation   Invited Session, 6 papers
VI115-05 Control for Next Generation Wireless Networks   Open Invited Session, 9 papers
VI115-06 Distributed Optimization for Learning and Control in Smart Networks   Open Invited Session, 10 papers
VI115-07 Event-Triggered and Self-Triggered Control   Open Invited Session, 19 papers
VI115-08 Social Systems: Dynamics, Games and Control on Networks   Open Invited Session, 13 papers
VI115-09 Consensus   Regular Session, 30 papers
VI115-10 Control under Communication Constraints   Regular Session, 9 papers
VI115-11 Coordination of Multiple Vehicle Systems   Regular Session, 9 papers
VI115-12 Distributed Control and Estimation   Regular Session, 14 papers
VI115-13 Distributed Optimization for Large-Scale Systems   Regular Session, 9 papers
VI115-14 Multi-Agent Systems   Regular Session, 28 papers
VI115-15 Security of Networked Control Systems   Regular Session, 12 papers
VI115-16 Sensor Networks   Regular Session, 7 papers
VI115-01
Cooperative Control of Unmanned Aerial Vehicles: Reliability and Autonomy Invited Session
Chair: Wang, XiangkeNational University of Defense Technology
Co-Chair: Sun, ZhiyongEindhoven University of Technology (TU/e)
Organizer: Wang, XiangkeNational University of Defense Technology
Organizer: Liu, HaoBeihang University
Organizer: Sun, ZhiyongEindhoven University of Technology (TU/e)
Paper VI115-01.1 
PDF · Video · Angle-Based Formation Shape Control with Velocity Alignment (I)

Chen, LiangmingUniversity of Groningen
Cao, MingUniversity of Groningen
Sun, ZhiyongEindhoven University of Technology (TU/e)
Anderson, Brian D. O.Australian National Univ/NICTA
Li, ChuanjiangHarbin Institute of Technology
Keywords: Networked robotic systems, Coordination of multiple vehicle systems, Multi-agent systems
Abstract: With the rapid development of sensor technology, bearing/angle measurements are becoming cheaper and more reliable, which motivates the study of angle-based formation shape control. This work studies how to achieve angle-based formation control and velocity alignment at the same time, in which all agents can form a desired angle-rigid formation and translate with the same velocity simultaneously. The agents' communication topology for the achievement of velocity alignment is described by a connected graph, while the formation shape is determined by a set of angles that are associated with triangles within the formation and computed using bearing measurements. A simulation example validates the effectiveness of the theoretical results.
Paper VI115-01.2 
PDF · Video · Distributed Finite-Time Coordination Control for Networked Euler-Lagrange Systems under Directed Graphs (I)

Xu, TaoPeking University
Lv, YuezuSoutheast University
Duan, ZhishengPeking University
Keywords: Multi-agent systems, Consensus
Abstract: The distributed coordination problems for networked Euler-Lagrange systems are investigated in this paper, where both the distributed synchronization control and the distributed containment control are considered. Compared with the existing traditional asymptotically stable control laws, the desired cooperative control objectives of this paper can be realized in finite time, and the estimate of the settling times are explicitly provided. Another distinct feature of our work is that the communication interactions between neighboring agents are unidirectional, which is more practical in real applications. Finally, some simulation results are shown to validate the feasibility of the theoretical schemes.
Paper VI115-01.3 
PDF · Video · Dynamics of Generic Linear Agents Over Signed Networks without Structural Constraints (I)

Shi, LeiUniversity of Electronic Science and Technology of China
Chen, HongjianUniversity of Electronic Science and Technology of China
Cheng, YuhuaUniversity of Electronic Science and Technology of China
Zheng, Wei XingWestern Sydney University
Shao, JinliangUniversity of Electronic Science and Technology of China
Keywords: Consensus, Multi-agent systems, Control of networks
Abstract: Signed networks have been widely used to describe cooperative and competitive interactions in multiagent systems (MASs) so far. Most of the existing dynamics results of MASs on signed networks have a certain constraint on the network topology, that is, the network topology is required to have sufficient connectivity for realizing consensus or bipartite consensus. The highlight of this article is to extend the existing dynamics of MASs to a more general signed network, in which there are no any structural constraints on the topology. This general setting of the network topology unifies most existing models, such as consensus, bipartite consensus, and bipartite containment, which are usually analyzed separately in the same framework using different methods. Relying on a method of constructing cooperative auxiliary digraphs, it is theoretically proved that the agents in closed strong connected components with balanced structure and unbalanced structure gradually reach separately bipartite consensus and consensus, and the agents outside the closed strong connected components gradually enter the convex hull formed by the agents in the closed strong connected components, that is, achieving bipartite containment. Finally, a computer simulation is presented to verify the theoretical discovery.
Paper VI115-01.4 
PDF · Video · Fault-Tolerant Control for the Formation of Multiple Unknown Nonlinear Quadrotors Via Reinforcement Learning (I)

Zhao, WanbingBeihang University
Liu, HaoBeihang University
Lewis, Frank L.Univ of Texas at Arlington
Keywords: Networked robotic systems, Consensus and Reinforcement learning control, Multi-agent systems
Abstract: In this paper, the fault-tolerant control problem for the formation of unknown quadrotor team with nonlinearities, couplings, and actuator faults in the dynamics is investigated. A distributed observer is designed to estimate the position references for each quadrotor. A hierarchical control scheme is constructed including a fault-tolerant position controller to achieve the desired formation and a fault-tolerant attitude controller to track the attitude references. Reinforcement learning algorithms are designed to learn the optimal control policies of the position and attitude controllers. Simulation results are given to illustrate the effectiveness of the proposed controller.
Paper VI115-01.5 
PDF · Video · Optimal UAV Circumnavigation Control with Input Saturation Based on Information Geometry (I)

Yu, YangguangNational University of Defense Technology
Wang, XiangkeNational University of Defense Technology
Shen, LinchengNational University of Defense Technology
Keywords: Extremum seeking and model free adaptive control, Nonlinear adaptive control, Dynamic Networks
Abstract: In this paper, we investigate the problem of the optimal circumnavigation around a ground moving target for a fixed-wing unmanned aerial vehicle equipped with a radar. We propose an optimal circumnavigation control law which not only achieves the circumnavigation of a UAV around a moving target, but also maximizes the utilization of the sensor information. Firstly, an optimization criterion re ecting the extent of the sensor information utilization is established based on the Fisher information. Then, based on a neural network, an optimal circumnavigation control law with input saturation is designed. The result is a nearly optimal state feedback controller that has been tuned a priori off-line. Finally, a simulation is presented to demonstrate the validity and correctness of the proposed method.
Paper VI115-01.6 
PDF · Video · A Hierarchical Collision Avoidance Architecture for Multiple Fixed-Wing UAVs in an Integrated Airspace (I)

Wang, YajingNational University of Defense Technology
Wang, XiangkeNational University of Defense Technology
Zhao, ShulongNational University of Defense Technology
Shen, LinchengNational University of Defense Technology
Keywords: Coordination of multiple vehicle systems, Multi-agent systems
Abstract: This paper studies the collision avoidance problem for autonomous multiple fixed-wing UAVs in the complex integrated airspace. By studying and combining the online path planning method, the distributed model predictive control algorithm, and the geometric reactive control approach, a three-layered collision avoidance system integrating conflict detection and resolution procedures is developed for multiple fixed-wing UAVs modeled by unicycle kinematics subject to input constraints. The effectiveness of the proposed methodology is evaluated and validated via test results of comparative simulations under both deterministic and probabilistic sensing conditions.
Paper VI115-01.7 
PDF · Video · Consensus for Expressed and Private Opinions under Self-Persuasion (I)

Cheng, ChunFudan University
Luo, YunWuhan University
Yu, Changbin (Brad)Australian National University
Keywords: Multi-agent systems, Dynamic Networks, Consensus
Abstract: As recognized in psychological research, there is often a difference between an agent’s expressed opinion and private opinion (or belief). This occurs for different reasons, such as political correctness or peer pressure. The opinion expressed by an agent is the result of pressure to follow the (average) opinions expressed by the group to which the agent belongs, or to follow group norms. The agent’s private opinion is unknown to others, but evolved under the influence of other agents’ expressed opinions. This paper proposes an opinion formation model based on the theory of bounded confidence, and studies the dynamic process of expressed and private opinions in time-varying networks. At the same time, the self-persuasion effect of agents in the dissonance between expressed and private opinions is considered. Here, group pressure establishes the motive force from private opinion to expressed opinion, while self-persuasion establishes the reverse connection. We find that group pressure can effectively reduce the gap of opinions between the group, but does not always promote consensus. Furthermore, the self-persuasion effect of agents can ensure the realization of group consensus.
Paper VI115-01.8 
PDF · Video · Coordination Control of Double-Integrator Systems with Time-Varying Weighted Inputs (I)

Greiff, Carl MarcusLund University
Sun, ZhiyongEindhoven University of Technology (TU/e)
Robertsson, AndersLTH, Lund University
Keywords: Distributed control and estimation, Coordination of multiple vehicle systems, Networked embedded control systems
Abstract: This paper considers coordination control of double-integrator systems and proposes general control laws involving time-varying inputs. The nominal control input is weighted by time-varying (time-dependent or state-dependent) positive definite matrices, providing more freedoms in defining the control tasks. We present sufficient conditions to ensure the asymptotic convergence of double-integrator networked systems in this context, and support the theoretical results by several application examples. This includes distance-based multi-agent formation control and power network systems with unknown inertia matrices.
Paper VI115-01.9 
PDF · Video · Finite-Time Distributed Convex Optimization with Zero-Gradient-Sum Algorithms (I)

Wu, ZizhenPeking University
Li, ZhongkuiPeking University
Keywords: Consensus, Multi-agent systems, Distributed control and estimation
Abstract: This article considers the distributed finite-time optimization problem of multi-agent systems within the Zero-Gradient-Sum (ZGS) framework. We employ a distributed algorithm to drive the estimate of each agent to converge to the optimal solution of the global objective function, the sum of the local objectives. In a general case with non-quadratic local functions, we can obtain a finite-time convergence. Furthermore, when all the local cost functions are quadratic, the proposed algorithm can achieve a fixed-time result such that the upper bound of settling time can be estimated regardless of the initial conditions. Considering that the communication network may be affected by some external disturbances, we also extend to consider the case with switching topologies. Finally, the algorithms are demonstrated via an example simulation.
VI115-02
Resilience Analysis, Security Countermeasures and Privacy Protection in
Distributed State Estimation
Invited Session
Chair: Yang, WenEast China University of Science and Techonology
Co-Chair: Zhang, HengHuaihai Institute of Technology
Organizer: Yang, WenEast China University of Science and Techonology
Organizer: Tang, YangEast China University of Science and Technology
Organizer: Zhang, HengHuaihai Institute of Technology
Organizer: Zheng, MinghuiUniversity at Buffalo
Paper VI115-02.1 
PDF · Video · Reinforcement Learning Based Anti-Jamming Schedule in Cyber Physical Systems (I)

Gan, RuimengUniversity of Electronic Science and Technology of China
Xiao, YueNational Key Laboratory of Science and Technology on Communicati
Shao, JinliangUniversity of Electronic Science and Technology of China
Zhang, HengHuaihai Institute of Technology
Zheng, Wei XingWestern Sydney University
Keywords: Secure networked control systems, Control and estimation with data loss
Abstract: In this paper, the security issue of cyber-physical systems is investigated, where the observation data is transmitted from a sensor to an estimator through wireless channels disturbed by an attacker. The failure of this data transmission occurs, when the sensor accesses the channel that happens to be attacked by the jammer. Since the system performance measured by the estimation error depends on whether the data transmission is a success, the problem of selecting the channel to alleviate the attack e ect is studied. Moreover, the state of each channel is time-variant due to various factors, such as path loss and shadowing. Motivated by energy conservation, the problem of selecting the channel with the best state is also considered. With the help of cognitive radio technique, the sensor has the ability of selecting a sequence of channels dynamically. Based on this, the problem of selecting the channel is resolved by means of reinforcement learning to jointly avoid the attack and enjoy the channel with the best state. A corresponding algorithm is presented to obtain the sequence of channels for the sensor, and its e ectiveness is proved analytically. Numerical simulations further verify the derived results.
Paper VI115-02.2 
PDF · Video · A "Safe Kernel" Approach for Resilient Multi-Dimensional Consensus (I)

Yan, JiaqiNanyang Technological University, Singapore
Mo, YilinTsinghua University
Li, XiuxianNanyang Technological University
Wen, ChangyunNanyang Technological University
Keywords: Consensus, Secure networked control systems
Abstract: This paper considers the resilient multi-dimensional consensus problem in networked systems, where some of the agents might be malicious (or faulty). We propose a multi-dimensional consensus algorithm, where at each time step each healthy agent computes a "safe kernel" based on the information from its neighbors, and modifies its own state towards a point inside the kernel. Assuming that the number of malicious agents is locally (or globally) upper bounded, sufficient conditions on the network topology are presented to guarantee that the benign agents exponentially reach an agreement within the convex hull of their initial states, regardless of the actions of the misbehaving ones. It is also revealed that the graph connectivity and robustness required to achieve the resilient consensus increases linearly with respect to the dimension of the agents’ state, indicating the existence of a trade-off between the low communication cost and system security. Numerical examples are provided in the end to validate the theoretical results.
Paper VI115-02.3 
PDF · Video · Intrusion Detection of Industrial Control System Based on Double-Layer One-Class Support Vector Machine (I)

Zhang, Wen-AnZhejiang University of Technology
Miao, YinfengZJUT
Wu, QiCollege of Information Engineering, Zhejiang University of Techn
Yu, LiZhejiang Univ of Technology
Shi, XiufangZhejiang University of Technology
Keywords: Secure networked control systems
Abstract: In this paper, the stealthy attack detection in industrial control system (ICS) is studied, and a new detection method is proposed from the perspective of signal analysis. The method consists of a double-layer one-class support vector machine model (DL-OCSVM), where the first-layer model is the standard OCSVM, and the second-layer model is obtained by incremental learning based on the former. The wavelet decomposition is used to extract the physical characteristics of the ICS. The KKT condition and the adjacent classification interval are adopted to reduce the training samples, improving the learning rate and system scalability. In addition, the designed boundary samples are employed for incremental learning, avoiding overfitting and reducing false positives rate (FPR). The experimental results show that the proposed method has high detection rate and low FPR for stealthy attacks, and is more suitable for precision machining process.
Paper VI115-02.4 
PDF · Video · Optimal Online Transmission Schedule for Remote State Estimation Over a Hidden Markovian Channel (I)

Sun, BowenSoutheast University
Cao, XianghuiSoutheast University
Wang, LeSoutheast University
Sun, ChangyinSoutheast University
Keywords: Control and estimation with data loss, Estimation and filtering
Abstract: This paper investigates the optimal transmission scheduling problem in remote state estimation systems over an unreliable wireless channel where the channel state evolves as a Markov chain. However, due to inaccurate observations of the channel state, the wireless channel is modeled as a hidden Markov chain. We propose a prediction algorithm based on the Viterbi algorithm to estimate the channel state. To save the wireless sensor's energy, we consider scheduling the transmission of sensor transmissions while balancing between estimation performance and sensor energy expenditure. By jointly considering performance and energy, we formulate the scheduling problem as a Markov decision process. We prove the existence of the optimal transmission policy and derive a threshold structure of the optimal strategy. Finally, the performance of the proposed method is evaluated through simulations.
VI115-03
Resilient and Networked Control of Complex Cyber-Physical Systems Invited Session
Chair: Zhao, YuNorthwestern Polytechnical University
Co-Chair: Liu, HaoBeihang University
Organizer: Wang, BohuiNanyang Technological University
Organizer: Zhao, YuNorthwestern Polytechnical University
Organizer: Shen, ChaoXi'an Jiaotong University
Organizer: Liu, HaoBeihang University
Organizer: Zhang, DongNorthwestern Polytechnical University
Organizer: Liang, XiaolingDalian Maritime University
Organizer: Zhang, BinUniversity of South Carolina
Organizer: Zhang, LangwenSouth China University of Technology
Paper VI115-03.1 
PDF · Video · Control Lyapunov Function Based Finite-Horizon Optimal Control for Repointing of a Spacecraft (I)

Geng, YuanzhuoHarbin Institute of Technology
Li, ChuanjiangHarbin Institute of Technology
Guo, YanningHarbin Institute of Technology
Biggs, James DouglasPolitecnico Di Milano
Keywords: Nonlinear adaptive control, Adaptive gain scheduling autotuning control and switching control, Optimal control of hybrid systems
Abstract: This paper addresses the problem of optimally repointing the optical axis of a spacecraft to align with the target direction. A new metric defining the repointing error is proposed where the corresponding kinematic equations provide a simple and convenient form for control design. The proposed control integrates a Control Lyapunov Function (CLF) approach with a sliding mode controller which simultaneously guarantees the optimality and robustness of the closed-loop system. Firstly, a CLF based control scheme is used to ensure that the state optimally converges to the sliding surface. Then a fixed-time non-singular terminal sliding mode controller is employed to provide robust convergence to the origin along the sliding surface. The convergence time is finite for any initial states and is thus useful for applications with critical time constraints. The region of attraction and convergence time is analyzed. Finally, numerical investigations are conducted to verify the effectiveness and superiority of the proposed algorithm with respect to the classical CLF method.
Paper VI115-03.2 
PDF · Video · Moving Area Tracking Formation Control of Multiple Autonomous Agents (I)

Zhang, WenfeiNorthwestern Polytechnical University
Zhao, YuNorthwestern Polytechnical University
Keywords: Multi-agent systems
Abstract: This paper investigates a moving area tracking formation control (MATFC) problem of multiple autonomous agents, which aims at driving a group of agents to achieve a desired formation configuration and track a moving area. By using local information interaction among agents, a distributed MATFC protocol is proposed for single integrator dynamics. Without requiring the center of the sub-area is bounded, the MATFC problem obtains greater application potential. During the moving process, the formation size can be regulated in real time to adapt the complicated environment through a scaling parameter. By adding a rotation matrix, the spatial orientation of each agent is capable of transforming in different cases. Then, based on the Lyapunov stability theory, it is verified that the objective of MATFC problem can be achieved under the proposed control protocol. Finally, numerical simulation results are shown to further demonstrate the effectiveness of the designed MATFC protocol.
Paper VI115-03.3 
PDF · Video · A Probabilistic Time-Constrained Based Heuristic Path Planning Algorithm in Warehouse Multi-AGV Systems (I)

Lian, YindongSouth China University of Technology
Xie, WeiSouth China University of Technology
Zhang, LangwenSouth China University of Technology
Keywords: Coordination of multiple vehicle systems, Multi-agent systems, Networked robotic systems
Abstract: This paper mainly focuses on the path planning algorithm of multi-AGV system in the warehouse environment. We first analyze and model the path network of multiple AGVs based on dynamic stochastic network theory. Then, a probabilistic time constraint is added in the process of the well-known A* heuristic algorithm, and the solution of the time cost is proposed based on probability theory. Furthermore, a multi-AGV conflict avoidance strategy suitable for heuristic planning algorithms is achieved in combination with queuing mechanism. Finally, numerical simulation experiments of the warehouse multi-AGV system are realized and demonstrate the effectiveness of the proposed algorithm.
Paper VI115-03.4 
PDF · Video · Distributed Fault Detection of Nonlinear Process Systems with Senor Failures (I)

Zhang, LangwenSouth China University of Technology
Xie, WeiSouth China University of Technology
Lian, YindongSouth China University of Technology
Keywords: Distributed control and estimation, Fault detection and diagnosis, Filtering and smoothing
Abstract: A distributed fault detection scheme is presented in this work to deal with the senor failures in a nonlinear process system. Firstly, a residual generator is derived, in which the fault signal is generated by introducing a residual signal. Then, a distributed extended Kalman Filter (EKF) is designed to estimate the unmeasurable system states. Finally, the proposed distributed EKF is used for the fault detection and isolation in a distributed framework. By applying the distributed fault detection scheme to a completely stirred tank reactor process, it is shown that the proposed scheme has ability to monitor the sensor faults automatically.
Paper VI115-03.5 
PDF · Video · Distributed Event-Triggered Consensus of Multi-Agent Systems with Input Delay (I)

Li, YunhanBeijing Institute of Technology
Zhang, PengyuChina Aerospace Science and Technology Corporation
Wang, ChunyanBeijing Institute of Technology
Wang, DandanBeijing Institute of Technology
Wang, JiananBeijing Institute of Technology
Keywords: Event-based control, Multi-agent systems, Distributed control and estimation
Abstract: This paper investigates distributed event-triggered consensus control for multi-agent systems with input delay. To deal with input delay, the original system is converted to a delay-free system via Artstein-Kwon-Pearson reduction transformation method. Distributed event-triggered protocols are designed to alleviate the communication burden of the agents. The system convergence is validated by using Lyapunov stability analysis and solving linear matrix inequality function. Furthermore, it is proved that the system does not display Zeno behavior under the proposed event-triggering function, and thus, consistent triggering is excluded from the system. A simulation example is given to demonstrate the effectiveness of the control algorithm.
Paper VI115-03.6 
PDF · Video · False Data Injection Attacks for Networked Control Systems with Sensor Fault and Actuator Saturation (I)

Geng, QingYanshan University
Liu, FucaiYanshan University
Li, YafengYanshan University
Keywords: Control of networks, Dynamic Networks
Abstract: This paper presents the design problem of false data injection (FDI) attacks against the networked predictive control (NPC) strategy, where the sensor fault and actuator saturation are considered. An estimator is designed to estimate system states and sensor fault simultaneously. A predictive controller which can generate a sequence of predictive signals is designed to actively compensate the time-varying delays for the networked control system (NCS). A sufficient condition is derived for stability of the NCS by a switched system theory. Finally, a numerical simulation demonstrates the effectiveness of proposed method for the NCS.
VI115-04
Resilient Large-Scale Networks: Spreading and Bifurcation Invited Session
Chair: Pare, Philip E.KTH Royal Institute of Technology
Co-Chair: Johansson, Karl H.Royal Institute of Technology
Organizer: Pare, Philip E.KTH Royal Institute of Technology
Organizer: Gracy, SebinRoyal Institute of Technology, KTH
Organizer: Sandberg, HenrikKTH Royal Institute of Technology
Organizer: Johansson, Karl H.Royal Institute of Technology
Paper VI115-04.1 
PDF · Video · The Solution of the NIMFA Epidemic Model Around the Epidemic Threshold (I)

Prasse, BastianDelft University of Technology
Piet, Van MieghemDelft University of Technology
Keywords: Control of networks
Abstract: Non-linear differential equations are a common approach to modelling the spread of infectious diseases. Unfortunately, a closed-form solution is not known for the majority of epidemic models, which restricts an in-depth understanding of the evolution of the virus. In this work, we solve the differential equations of the NIMFA epidemic model around the epidemic threshold, provided that the initial viral state is small or proportional to the steady-state. The solution of the NIMFA model around the epidemic threshold is of particular importance for disease control measures that aim to eradicate the infectious disease.
Paper VI115-04.2 
PDF · Video · A Network SIS Meta-Population Model with Transportation Flow (I)

Ye, MengbinCurtin University
Liu, JiStony Brook University
Cenedese, CarloUniversity of Groningen
Sun, ZhiyongEindhoven University of Technology (TU/e)
Cao, MingUniversity of Groningen
Keywords: Multi-agent systems, Control over networks, Complex system management
Abstract: This paper considers a deterministic Susceptible-Infected-Susceptible (SIS) meta-population model for the spread of a disease in a strongly connected network, where each node represents a large population. Individuals can travel between the nodes (populations). We derive a necessary and sufficient condition for the healthy equilibrium to be the unique equilibrium of the system, and then in fact it is asymptotically stable for all initial conditions (a sufficient condition for exponential stability is also given). If the condition is not satisfied, then there additionally exists a unique endemic equilibrium which is exponentially stable for all nonzero initial conditions. We then consider time-delay in the travel between nodes, and further investigate the role of the mobility rate that governs the flow of individuals between nodes in determining the convergence properties. We find that sometimes, increasing mobility helps the system converge to the healthy equilibrium.
Paper VI115-04.3 
PDF · Video · Disagreement and Polarization in Two-Party Social Networks (I)

Yi, YuhaoRensselaer Polytechnic Institute
Patterson, StacyRensselaer Polytechnic Institute
Keywords: Consensus, Control of networks, Multi-agent systems
Abstract: We investigate disagreement and polarization in a social network with two polarizing sources of information. First, we define disagreement and polarization indices in two-party leader-follower models of opinion dynamics. We then give expressions for the indices in terms of a graph Laplacian. The expressions show a relationship between these quantities and the concepts of resistance distance and biharmonic distance. We next study the problem of designing the network so as to minimize disagreement and polarization. We give conditions for optimal disagreement and polarization, and further, we show that a linear combination of disagreement and polarization of the follower nodes is a convex function of the edge weights between followers. We propose algorithms to address some related continuous and discrete optimization problems and also present analytic results for some interesting examples.
Paper VI115-04.4 
PDF · Video · On the Stability of the Endemic Equilibrium of a Discrete-Time Networked Epidemic Model (I)

Liu, FangzhouTechnical University of Munich
Cui, ShaoxuanTechnical University of Munich
Li, XianweiNanyang Technological University
Buss, MartinTechnische Universitaet Muenchen
Keywords: Control over networks
Abstract: Networked epidemic models have been widely adopted to describe propagation phenomena. The endemic equilibrium of these models is of great significance in the field of viral marketing, innovation dissemination, and information diffusion. However, its stability conditions have not been fully explored. In this paper, we study the stability of the endemic equilibrium of a networked Susceptible-Infected-Susceptible (SIS) epidemic model with heterogeneous transition rates in a discrete-time manner. We show that the endemic equilibrium, if it exists, is asymptotically stable for any nontrivial initial condition. Under mild assumptions on initial conditions, we further prove that during the spreading process there exists no overshoot with respect to the endemic equilibrium. Finally, we conduct numerical experiments on real-world networks to illustrate our results.
Paper VI115-04.5 
PDF · Video · On a Network SIS Model with Opinion Dynamics (I)

Xuan, WeihaoUniversity of Leeds
Ren, RuijieUniversity of Leeds
Pare, Philip E.KTH Royal Institute of Technology
Ye, MengbinCurtin University
Ruf, SebastianNortheastern University
Liu, JiStony Brook University
Keywords: Multi-agent systems, Dynamic Networks, Consensus
Abstract: This paper proposes a network continuous-time susceptible-infected-susceptible (SIS) model coupled with individual opinion dynamics, where the opinion dynamic models an individual's perceived severity of illness or perceived susceptibility. The effects of opinion dynamics on the network SIS model are studied by analyzing the limiting behaviors of the model, equilibria of the system and their stability.
Paper VI115-04.6 
PDF · Video · Stability and Phase Transitions of Dynamical Flow Networks with Finite Capacities (I)

Leonardo, MassaiPolitecnico Di Torino
Como, GiacomoPolitecnico Di Torino
Fagnani, FabioPolitecnico Di Torino
Keywords: Control over networks, Multi-agent systems, Dynamic Networks
Abstract: We study deterministic continuous-time lossy dynamical flow networks with constant exogenous demands, fixed routing, and finite flow and buffer capacities. In the considered model, when the total net flow in a cell —consisting of the difference between the total flow directed towards it minus the outflow from it— exceeds a certain capacity constraint, then the exceeding part of it leaks out of the system. The ensuing network flow dynamics is a linear saturated system with compact state space that we analyse using tools from monotone systems and contraction theory. Specifically, we prove that there exists a set of equilibria that is globally asymptotically stable. Such equilibrium set reduces to a single globally asymptotically stable equilibrium for generic exogenous demand vectors. Moreover, we show that the critical exogenous demand vectors giving rise to non-unique equilibria correspond to phase transitions in the asymptotic behavior of the dynamical flow network.
VI115-05
Control for Next Generation Wireless Networks Open Invited Session
Chair: Gatsis, KonstantinosUniversity of Oxford
Co-Chair: Baumann, DominikMax Planck Institute for Intelligent Systems
Organizer: Baumann, DominikMax Planck Institute for Intelligent Systems
Organizer: Gatsis, KonstantinosUniversity of Pennsylvania
Organizer: Johansson, Karl H.Royal Institute of Technology
Organizer: Trimpe, SebastianMax Planck Institute for Intelligent Systems
Paper VI115-05.1 
PDF · Video · Stability Analysis for Nonlinear Weakly Hard Real-Time Control Systems (I)

Hertneck, MichaelUniversity of Stuttgart
Linsenmayer, SteffenUniversity of Stuttgart
Allgower, FrankUniversity of Stuttgart
Keywords: Networked embedded control systems, Control over networks, Control under communication constraints
Abstract: This paper considers the stability analysis for nonlinear sampled-data systems with failures in the feedback loop. The failures are caused by shared resources, and modeled by a weakly hard real-time (WHRT) dropout description. The WHRT dropout description restricts the considered dropout sequences with a non-probabilistic, window based constraint, that originates from schedulability analysis. The proposed approach is based on the emulation of a controller for the nonlinear sampled-data system from a continuous-time feedback. The emulation technique is extended and combined with non-monotonic Lyapunov functions and a graph description for the WHRT constraints to guarantee asymptotic stability. The effectiveness of the proposed approach is illustrated with a numerical example from literature.
Paper VI115-05.2 
PDF · Video · Predictably Reliable Real-Time Transport Services for Wireless Cyber-Physical Systems (I)

Schmidt, AndreasSaarland Informatics Campus
Gil Pereira, PabloSaarland Informatics Campus
Herfet, ThorstenIntel Visual Computing Institute, Saarland University
Keywords: Control under communication constraints, Networked embedded control systems, Control over networks
Abstract: Cyber-physical systems increasingly leverage wireless networks for distributed control applications. In these systems, control and communication must find explicit agreements on the resilience and age-of-information (AoI) provided by the transport services to ensure stability. We present PRRT and its unique features to provide a predictably reliable real-time service that can fulfil these agreements. These features include cross-layer pacing, i.e. allowing an application to adapt to the system's bottleneck to achieve predictably low AoI. Finally, we highlight future directions for the transport service provided by PRRT with respect to its usage in constrained devices, where, e.g., energy demands play an important role.
Paper VI115-05.3 
PDF · Video · Learning to Control Over Unknown Wireless Channels (I)

Gatsis, KonstantinosUniversity of Oxford
Pappas, George J.Univ of Pennsylvania
Keywords: Learning for control, Control over networks
Abstract: Emerging control applications in the Internet-of-Things are increasingly relying on communication networks and wireless channels to close the loop. Traditional model-based approaches, i.e., assuming a known wireless channel model, are focused on analyzing stability and designing appropriate controller structures. Such modeling is challenging as wireless channels are typically unknown a priori and only available through data samples. In this work we aim to design data-based controllers using channel samples and provide high confidence guarantees on the performance of these controllers when deployed over the actual unknown channel. To achieve these results we combine statistical learning (concentration inequalities) with structural properties of our problem (monotonicity with respect to the unknown channel parameters), and also provide sample complexity analysis.
Paper VI115-05.4 
PDF · Video · Real-Time Distributed Automation of Road Intersections (I)

Molinari, FabioTechnische Universitaet Berlin
Katriniok, AlexanderFord Research & Innovation Center (RIC)
Raisch, JoergTechnische Universitaet Berlin
Keywords: Coordination of multiple vehicle systems, Distributed control and estimation, Consensus
Abstract: The topic of this paper is the design of a fully distributed and real-time capable control scheme for the automation of road intersections. State of the art Vehicle-to-Vehicle (V2V) communication technology is adopted. Vehicles distributively negotiate crossing priorities by running a Consensus-Based Auction Algorithm (CBAA-M). Then, each agent solves a nonlinear Model Predictive Control (MPC) problem that computes the optimal trajectory avoiding collisions with higher priority vehicles and deciding the crossing order. The scheme is shown to be real-time capable and able to respond to sudden priority changes, e.g. if a vehicle gets an emergency call. Simulations reinforce theoretical results.
Paper VI115-05.5 
PDF · Video · 1 kHz Remote Control of a Balancing Robot with Wi-Fi-In-The-Loop (I)

Branz, FrancescoUniversity of Padova
Antonello, RiccardoUniversity of Padova
Pezzutto, MatthiasUniversity of Padova
Tramarin, FedericoUniversity of Padova
Schenato, LucaUniv of Padova
Keywords: Control over networks, Control under communication constraints, Networked embedded control systems
Abstract: Countless industrial applications can potentially benefit from the implementation of wireless control systems, leading to a widespread research effort to investigate new solutions in the field. Nevertheless, currently available wireless communication standards for industrial automation are not able to achieve high control frequencies. In particular, time-critical applications (e.g. industrial robotics and manipulation) require high sampling frequencies to be properly implemented. The higher throughput provided by IEEE 802.11 (Wi-Fi) can theoretically tame critical applications, although reliability is a key issue. This work presents an innovative approach to the control over wireless problem: Wi-Fi is adopted to increase the achievable control rates up to 1 kHz, while reliability is guaranteed by mitigating communication flaws through model-based estimation techniques. The core of the proposed approach relies on a modified Kalman filter that exploits a buffer of incoming measures to account for delayed data packets. The proposed solution is validated through a hardware-in-the-loop experiment that features actual Wi-Fi hardware and a commercial embedded PC board. The obtained results give a preliminary, yet valuable, validation of the proposed approach testing the solution on relevant hardware.
Paper VI115-05.6 
PDF · Video · Saving Tokens in Rollout Control with Token Bucket Specification (I)

Jaumann, FlorianUniversity of Stuttgart
Wildhagen, StefanUniversity of Stuttgart
Allgower, FrankUniversity of Stuttgart
Keywords: Control over networks, Control under communication constraints, Event-based control
Abstract: We consider a communication network over which transmissions must fulfill the so-called token bucket traffic specification, with a rollout (i.e., predictive) controller that both schedules transmissions and computes the corresponding control values. In the token bucket specification, a transmission is allowed if the current level of tokens is above a certain threshold. Recently, it has been shown that having a full bucket at the time of a set point change significantly improves the control performance as compared to when the bucket level is low. In this work, we develop mechanisms that guarantee that the bucket fills up after the controlled plant has converged to a set point. To do this, we consider two different setups. First, we consider that all transmissions over the network must fulfill the token bucket specification and show convergence to the upper sector of the bucket by adding a slight terminal cost on the bucket level. Afterwards, we consider a modified network which additionally features a direct link over which transmissions need not fulfill the token bucket specification. In this setup, we prove convergence of the bucket level exactly to the upper rim. These mechanisms enable a similar level of flexibility as event-triggered control: In converged state, little communication is used while in precarious operating conditions, a burst of transmissions is possible. Other than event-triggered approaches, the proposed methods allow to specify the network traffic beforehand by means of the token bucket. Lastly, we validate the proposed approaches in a numerical example.
Paper VI115-05.7 
PDF · Video · Transmission Scheduling for Remote Estimation with Multi-Packet Reception under Multi-Sensor Interference (I)

Pezzutto, MatthiasUniversity of Padova
Schenato, LucaUniv of Padova
Dey, SubhrakantiUppsala University
Keywords: Sensor networks, Control and estimation with data loss, Control under communication constraints
Abstract: In networked control systems, due to competing demands on bandwidth and energy constraints, sensor scheduling is an important problem for remote estimation and control tasks. Traditionally, a single sensor is scheduled in each resource block to avoid interference or collisions so that the probability of packet loss is reduced. However, receiving multiple packets from different sources under interference is routinely achieved in wireless networks using multi-packet reception techniques. In this work, we explore the problem of sensor scheduling for remote estimation when the estimator is able to simultaneously receive multiple packets. We use the typical signal-to-interference-and-noise-ratio (SINR) based capture model to compute the packet arrival probabilities. Then an optimal scheduling policy is determined by minimizing expected estimation error covariance subject to a constraint on the average number of total transmissions. In the case of two sensors, for a scalar system and for a decoupled two-dimensional system, we show that allowing multiple simultaneous transmissions can improve the quality of the estimation achieving lower energy consumptions and we provide structural results on the optimal policies. Numerical results illustrate the benefits of multi-packet reception in remote estimation.
Paper VI115-05.8 
PDF · Video · Resource Allocation in Large-Scale Wireless Control Systems with Graph Neural Networks (I)

Lima, ViníciusUniversity of Pennsylvania
Eisen, MarkIntel Corporation
Gatsis, KonstantinosUniversity of Oxford
Ribeiro, AlejandroUniversity of Pennsylvania
Keywords: Control over networks, Learning for control, Consensus and Reinforcement learning control
Abstract: Modern control systems routinely employ wireless networks to exchange information between a large number of plants, actuators and sensors. While wireless networks are defined by random, rapidly changing conditions that challenge common control design assumptions, properly allocating communication resources helps to maintain operation reliable. Designing resource allocation policies is usually challenging and requires explicit knowledge of the system and communication dynamics, but recent works have successfully explored deep reinforcement learning techniques to find optimal model-free resource allocation policies. Deep reinforcement learning algorithms do not necessarily scale well, however, which limits the immediate generalization of those approaches to large-scale wireless control systems. In this paper we discuss the use of reinforcement learning and graph neural networks (GNNs) to design model-free, scalable resource allocation policies. On the one hand, GNNs generalize the spatial-temporal convolutions present in convolutional neural networks (CNNs) to data defined over arbitrary graphs. In doing so, GNNs manage to exploit local regular structure encoded in graphs to reduce the dimensionality of the learning space. The architecture of the wireless network, on the other, defines an underlying communication graph that can be used as basis for a GNN model. Numerical experiments show the learned policies outperform baseline resource allocation solutions.
Paper VI115-05.9 
PDF · Video · A Clustering Approach to Edge Controller Placement in Software-Defined Networks with Cost Balancing

Soleymanifar, RezaUniversity of Illinois at Urbana-Champaign
Srivastava, AmberUniversity of Illinois at Urbana Champaign
Beck, Carolyn L.Univ. of Illinois at Urbana-Champaign
Salapaka, SrinivasaUniv of Illinois
Keywords: Machine learning, Control over networks, Sensor networks
Abstract: In this work we introduce two novel maximum entropy based clustering algorithms to address the problem of Edge Controller Placement (ECP) in wireless edge networks. These networks lie at the core of the fifth generation (5G) wireless systems and beyond. Our algorithms, ECP-LL and ECP-LB, address the dominant leader-less and leader-based controller placement topologies and have linear computational complexity in terms of network size, number of clusters and dimensionality of data. Each algorithm places controllers close to edge node clusters and not far away from other controllers to maintain a reasonable balance between synchronization and delay costs. While the ECP problem can be expressed as a multi-objective mixed integer non-linear program (MINLP), our algorithms outperform state of art MINLP solver, BARON both in terms of accuracy and speed. Our proposed algorithms have the competitive edge of avoiding poor local minima through a Shannon entropy term in the clustering objective function. Most ECP algorithms are highly susceptible to poor local minima and greatly depend on initialization.
VI115-06
Distributed Optimization for Learning and Control in Smart Networks Open Invited Session
Chair: Notarstefano, GiuseppeUniversity of Bologna
Co-Chair: Notarnicola, IvanoUniversity of Bologna
Organizer: Notarstefano, GiuseppeUniversity of Bologna
Organizer: Notarnicola, IvanoUniversity of Bologna
Organizer: Farina, FrancescoUniversity of Bologna
Paper VI115-06.1 
PDF · Video · Communication-Efficient Variance-Reduced Stochastic Gradient Descent (I)

Shokri Ghadikolaei, HosseinEPFL
Magnusson, SindriKTH Royal Institute of Technology
Keywords: Machine learning, Distributed optimisation for large-scale systems, Control under communication constraints
Abstract: We consider the problem of communication efficient distributed optimization where multiple nodes exchange important algorithm information in every iteration to solve large problems. In particular, we focus on the stochastic variance-reduced gradient and propose a novel approach to make it communication-efficient. That is, we compress the communicated information to a few bits while preserving the linear convergence rate of the original uncompressed algorithm. Comprehensive theoretical and numerical analyses on real datasets reveal that our algorithm can significantly reduce the communication complexity, by as much as 95%, with almost no noticeable penalty. Moreover, it is much more robust to quantization (in terms of maintaining the true minimizer and the convergence rate) than the state-of-the-art algorithms for solving distributed optimization problems. Our results have important implications for using machine learning over internet-of-things and mobile networks.
Paper VI115-06.2 
PDF · Video · Combining ADMM and Tracking Over Networks for Distributed Constraint-Coupled Optimization (I)

Falsone, AlessandroPolitecnico Di Milano
Notarnicola, IvanoUniversity of Bologna
Notarstefano, GiuseppeUniversity of Bologna
Prandini, MariaPolitecnico Di Milano
Keywords: Distributed optimisation for large-scale systems, Control over networks, Consensus
Abstract: In this paper, we propose a novel distributed algorithm to address constraint-coupled optimization problems in which agents in a network aim at cooperatively minimizing the sum of local objective functions subject to individual constraints and a common, linear coupling constraint. Our optimization scheme embeds a dynamic average consensus protocol in the (parallel) Alternating Direction Method of Multipliers (ADMM) to design a fully distributed algorithm. More precisely, the dual variable update step of the master node in ADMM is now performed locally by the agent, which update their own copy of the dual variable in a consensus-based scheme using a dynamic average mechanism to track the coupling constraint violation. Under convexity, we show convergence of the primal solution estimates to an optimal solution of the constraint-coupled target problem. A numerical example supports the theoretical results.
Paper VI115-06.3 
PDF · Video · Cloud-Based Collaborative Learning of Optimal Feedback Controllers (I)

Breschi, ValentinaPolitecnico Di Milano
Ferrarotti, LauraIMT School for Advanced Studies, Lucca
Bemporad, AlbertoIMT Institute for Advanced Studies Lucca
Keywords: Consensus and Reinforcement learning control, Control over networks
Abstract: Industrial systems deployed in mass production, such as automobiles, can greatly benefit from sharing selected data among them through the cloud to self-adapt their control laws. The reason is that in mass production systems are clones of each other, designed, constructed, and calibrated by the manufacturer in the same way, and thus they share the same nominal dynamics. Hence, sharing information during closed-loop operations can dramatically help each system to adapt its local control laws so to attain its own goals, in particular when optimal performance is sought. This paper proposes an approach to learn optimal feedback control laws for reference tracking via a policy search technique that exploits the similarities between systems. By using resources available locally and on the cloud, global and local control laws are concurrently synthesized through the combined use of the alternating direction method of multipliers (ADMM) and stochastic gradient descent (SGD). The enhancement of learning performance due to sharing knowledge on the cloud is shown in a simple numerical example.
Paper VI115-06.4 
PDF · Video · DISROPT: A Python Framework for Distributed Optimization (I)

Farina, FrancescoUniversity of Bologna
Camisa, AndreaUniversity of Bologna
Testa, AndreaUniversità Di Bologna
Notarnicola, IvanoUniversity of Bologna
Notarstefano, GiuseppeUniversity of Bologna
Keywords: Distributed optimisation for large-scale systems, Distributed control and estimation, Machine learning
Abstract: In this paper we introduce DISROPT, a Python package for distributed optimization over networks. We focus on cooperative set-ups in which an optimization problem must be solved by peer-to-peer processors (without central coordinators) that have access only to partial knowledge of the entire problem. To reflect this, agents in DISROPT are modeled as entities that are initialized with their local knowledge of the problem. Agents then run local routines and communicate with each other to solve the global optimization problem. A simple syntax has been designed to allow for an easy modeling of the problems. The package comes with many distributed optimization algorithms that are already embedded. Moreover, the package provides full-fledged functionalities for communication and local computation, which can be used to design and implement new algorithms. DISROPT is available at github.com/disropt/disropt under the GPL license, with a complete documentation and many examples.
Paper VI115-06.5 
PDF · Video · Exponential Convergence for Distributed Optimization under the Restricted Secant Inequality Condition (I)

Yi, XinleiKTH Royal Institute of Technology
Zhang, ShengjunUniversity of North Texas
Yang, TaoNortheastern University
Chai, TianyouNortheastern Univ
Johansson, Karl H.Royal Institute of Technology
Keywords: Distributed optimisation for large-scale systems, Multi-agent systems, Consensus
Abstract: This paper considers the distributed optimization problem of minimizing a global cost function formed by a sum of local smooth cost functions by using local information exchange. A standard assumption for proving exponential/linear convergence of existing distributed first-order methods is strong convexity of the cost functions. This does not hold for many practical applications. In this paper, we propose a continuous-time distributed primal-dual gradient descent algorithm and show that it converges exponentially to a global minimizer under the assumption that the global cost function satisfies the restricted secant inequality condition. This condition is weaker than strong convexity and the global minimizer is not necessarily unique. Moreover, a discrete-time distributed primal-dual algorithm is developed from the continuous-time algorithm by Euler's approximation method, which also linearly converges to a global minimizer under the same condition. The theoretical results are illustrated by numerical simulations.
Paper VI115-06.6 
PDF · Video · Distributed Submodular Minimization Via Block-Wise Updates and Communications (I)

Testa, AndreaUniversità Di Bologna
Farina, FrancescoUniversity of Bologna
Notarstefano, GiuseppeUniversity of Bologna
Keywords: Distributed optimisation for large-scale systems, Multi-agent systems, Machine learning
Abstract: In this paper we deal with a network of computing agents with local processing and neighboring communication capabilities that aim at solving (without any central unit) a submodular optimization problem. The cost function is the sum of many local submodular functions and each agent in the network has access to one function in the sum only. In this distributed set-up, in order to preserve their own privacy, agents communicate with neighbors but do not share their local cost functions. We propose a distributed algorithm in which agents resort to the Lovàsz extension of their local submodular functions and perform local updates and communications in terms of single blocks of the entire optimization variable. Updates are performed by means of a greedy algorithm which is run only until the selected block is computed, thus resulting in a reduced computational burden. The proposed algorithm is shown to converge in expected value to the optimal cost of the problem, and an approximate solution to the submodular problem is retrieved by a thresholding operation. As an application, we consider a distributed image segmentation problem in which each agent has access only to a portion of the entire image. While agents cannot segment the entire image on their own, they correctly complete the task by cooperating through the proposed distributed algorithm.
Paper VI115-06.7 
PDF · Video · A Distributed Optimization Algorithm for Nash Bargaining in Multi-Agent Systems (I)

Camisa, AndreaUniversity of Bologna
Köhler, Philipp N.University of Stuttgart
Muller, Matthias A.Leibniz University Hannover
Notarstefano, GiuseppeUniversity of Bologna
Allgower, FrankUniversity of Stuttgart
Keywords: Distributed optimisation for large-scale systems, Distributed control and estimation, Multi-agent systems
Abstract: In this paper, we consider a multi-objective optimization problem over networks in which agents aim to maximize their own objective function, while satisfying both local and coupling constraints. This set up includes, e.g., the computation of optimal steady states in multi-agent control systems. Since fairness is a key feature required for the solution, we resort to Cooperative Game Theory and search for the Nash bargaining solution among all the efficient (or Pareto optimal) points of a bargaining game. We propose a negotiation mechanism among the agents to compute such a solution in a distributed way. The problem is reformulated as the maximization of a properly weighted sum of the objective functions. The proposed algorithm is then a two step procedure in which local estimates of the Nash bargaining weights are updated online and existing distributed optimization algorithms are applied. The proposed method is formally analyzed for a particular case, while numerical simulations are provided to corroborate the theoretical findings and to demonstrate its efficacy.
Paper VI115-06.8 
PDF · Video · On the Approachability Principle for Distributed Payoff Allocation in Coalitional Games (I)

Raja, Aitazaz AliDelft University of Technology
Grammatico, SergioDelft Univ. of Tech
Keywords: Multi-agent systems, Consensus, Control over networks
Abstract: In the context of coalitional games, we present a partial operator-theoretic characterization of the approachability principle and, based on this characterization, we interpret a particular distributed payoff allocation algorithm to be a sequence of time-varying paracontractions. Then, we also propose a distributed payoff allocation algorithm on time-varying communication networks. The state in the proposed algorithm converges to a consensus in the "CORE" set as desired. For the convergence analysis, we rely on an operator-theoretic property of paracontraction.
Paper VI115-06.9 
PDF · Video · Enhanced Gradient Tracking Algorithms for Distributed Quadratic Optimization Via Sparse Gain Design (I)

Carnevale, GuidoAlma Mater Studiorum Università Di Bologna
Bin, MichelangeloImperial College London
Notarnicola, IvanoUniversity of Bologna
Marconi, LorenzoUniv. Di Bologna
Notarstefano, GiuseppeUniversity of Bologna
Keywords: Distributed optimisation for large-scale systems, Multi-agent systems, Control of networks
Abstract: In this paper we propose a new control-oriented design technique to enhance the algorithmic performance of the distributed gradient tracking algorithm. We focus on a scenario in which agents in a network aim to cooperatively minimize the sum of convex, quadratic cost functions depending on a common decision variable. By leveraging a recent system-theoretical reinterpretation of the considered algorithmic framework as a closed-loop linear dynamical system, the proposed approach generalizes the diagonal gain structure associated to the existing gradient tracking algorithms. Specifically, we look for closed-loop gain matrices that satisfy the sparsity constraints imposed by the network topology, without however being necessarily diagonal, as in existing gradient tracking schemes. We propose a novel procedure to compute stabilizing sparse gain matrices based on the iterative solution of a nonlinear problem, in which each iteration deals with a linear problem. Numerical simulations are presented showing the enhanced performance of the proposed design compared to existing gradient tracking algorithms.
Paper VI115-06.10 
PDF · Video · A Proximal Point Approach for Distributed System State Estimation (I)

Fabris, MarcoUniversity of Padova
Michieletto, GiuliaUniversity of Padova
Cenedese, AngeloUniversity of Padova
Keywords: Distributed control and estimation, Multi-agent systems, Distributed optimisation for large-scale systems
Abstract: System state estimation constitutes a key problem in several applications involving multi-agent system architectures. This rests upon the estimation of the state of each agent in the group, which is supposed to access only relative measurements w.r.t. some neighbors state. Exploiting the standard least-squares paradigm, the system state estimation task is faced in this work by deriving a distributed Proximal Point-based iterative scheme. This solution entails the emergence of interesting connections between the structural properties of the stochastic matrices describing the system dynamics and the convergence behavior toward the optimal estimate. A deep analysis of such relations is provided, jointly with a further discussion on the penalty parameter that characterizes the Proximal Point approach.
VI115-07
Event-Triggered and Self-Triggered Control Open Invited Session
Chair: Heemels, MauriceEindhoven University of Technology
Co-Chair: Nowzari, CameronGeorge Mason University
Organizer: Heemels, MauriceEindhoven University of Technology
Organizer: Johansson, Karl H.Royal Institute of Technology
Organizer: Nowzari, CameronGeorge Mason University
Paper VI115-07.1 
PDF · Video · Decoupled Feedforward-Feedback Periodic Event-Triggered Control for Disturbance Rejection (I)

Aranda Escolástico, ErnestoUNED
Guinaldo, MariaUNED
Guzman, Jose LuisUniversity of Almeria
Dormido, SebastiánUNED
Keywords: Event-based control, Control under communication constraints
Abstract: In this paper, feedforward and feedback controllers are studied considering decoupled periodic event-triggering mechanisms for output and disturbance sensors. Stability and robustness conditions for linear systems are obtained considering transportation delays and actuator saturation following the Lyapunov-Krasovskii procedure. A numerical example shows that the proposed control strategy reduces the communication between sensors and controller significantly, while the system performance is not deteriorated.
Paper VI115-07.2 
PDF · Video · Event-Triggered Control for Extended Plants of Discrete-Time Linear Systems (I)

Ichihara, HiroyukiMeiji University
Sawada, KenjiThe University of Electro-Communications
Kobayashi, KoichiHokkaido University
Tarbouriech, SophieLAAS-CNRS
Keywords: Event-based control, Control over networks
Abstract: This paper deals with design methods of event-triggered control systems for discrete-time linear systems. An extended plant consisting of a given plant and a dynamical filter is considered and controlled by an event-triggered static output feedback. The triggering rule uses only the available signals and therefore is based on the difference between the triggered and non-triggered output signals. The paper deals with the co-design problem, that is the design of the triggering condition, the filter, and the controller simultaneously. Sufficient theoretical conditions are proposed in terms of linear matrix inequalities to ensure the asymptotic stability of the closed-loop system. Convex optimizations problems incorporate these conditions in order to optimize the closed-loop performance or to reduce the number of transmissions. Three numerical examples illustrate the design method of the triggering conditions as well as the simultaneous design method of the filter and controller.
Paper VI115-07.3 
PDF · Video · Event-Triggered Control Co-Design for Rational Systems (I)

Moreira, Luciano GonçalvesIFSUL
Gomes Da Silva Jr, Joao ManoelUniversidade Federal Do Rio Grande Do Sul (UFRGS)
Coutinho, DanielUniversidade Federal De Santa Catarina
Tarbouriech, SophieLAAS-CNRS
Keywords: Stability and stabilization of hybrid systems, Event-based control, Control under communication constraints
Abstract: This work deals with the problem of designing stabilizing event-triggered state-feedback controllers for rational systems. Using diferential algebraic representations and Lyapunov theory techniques, LMI-based conditions are derived to ensure regional asymptotic stability of the origin. These conditions are then cast into a convex optimization problem to the co-design of the event generator parameters and the state-feedback gain in order to reduce the controller updates while ensuring the asymptotic stability of the origin with respect to a given set of admissible initial conditions. The proposed methodology is illustrated by means of a numerical example.
Paper VI115-07.4 
PDF · Video · Scalable Traffic Models for Scheduling of Linear Periodic Event-Triggered Controllers (I)

de Albuquerque Gleizer, GabrielTU Delft
Mazo Jr, ManuelTU Delft
Keywords: Event-based control, Reachability analysis, verification and abstraction of hybrid systems, Control over networks
Abstract: This paper addresses the problem of modeling and scheduling the transmissions generated by multiple event-triggered control (ETC) loops sharing a network. We present a method to build a finite-state similar model of the traffic generated by periodic ETC (PETC), which by construction mitigates the combinatorial explosion that is typical of symbolic models. The model is augmented with early triggering actions that can be used by a scheduler. The complete networked control system is then modeled as a network of timed game automata, for which existing tools can generate strategies that avoids communication conflicts, while keeping early triggers to a minimum. Our proposed model is relatively fast to build and is the first to constitute an exact simulation. Finally, we demonstrate modeling and scheduling for a numerical example.
Paper VI115-07.5 
PDF · Video · A Resource-Aware Approach to Self-Triggered Model Predictive Control (I)

Wildhagen, StefanUniversity of Stuttgart
Jones, Colin N.EPFL
Allgower, FrankUniversity of Stuttgart
Keywords: Event-based control, Control under communication constraints, Control under computation constraints
Abstract: In this paper, we consider a self-triggered formulation of model predictive control. In this variant, the controller decides at the current sampling instant itself when the next sample should be taken and the optimization problem be solved anew. We incorporate a pointwise-in-time resource constraint into the optimization problem, whose exact form can be chosen by the user. Thereby, the proposed scheme is made resource-aware with respect to a universal resource, which may pertain in practice for instance to communication, computation, energy or financial resources. We show that by virtue of the pointwise-in-time constraints, also a transient and an asymptotic average constraint on the resource usage are guaranteed. Furthermore, we derive conditions on the resource under which the proposed scheme achieves recursive feasibility and convergence. Finally, we demonstrate our theoretical results in a numerical example.
Paper VI115-07.6 
PDF · Video · Event-Triggered PI Control of Time-Delay Systems with Parametric Uncertainties (I)

Iwaki, TakuyaKTH Royal Institute of Technology
Fridman, EmiliaTel-Aviv Univ
Johansson, Karl H.Royal Institute of Technology
Keywords: Event-based control, Control over networks
Abstract: This paper studies sampled-data implementation of event-triggered PI control for time-delay systems with parametric uncertainties. The systems are given by continuous-time linear systems with parameter uncertainty polytopes. We propose an event-triggered PI controller, in which the controller transmits its signal to the actuator when its relative value goes beyond a threshold. A state-space formulation of the Smith predictor is used to compensate the time-delay. An asymptotic stability condition is derived in the form of LMIs using a Lyapunov-Krasovskii functional. Numerical examples illustrate that our proposed controller reduces the communication load without performance degradation and despite plant uncertainties.
Paper VI115-07.7 
PDF · Video · Strongly Non-Zeno Event-Triggered Wireless Clock Synchronization (I)

Berneburg, JamesGeorge Mason University
Garcia, EloyAir Force Research Laboratory
Gerlach, AdamUnited States Air Force
Casbeer, David WellmanAir Force Research Laboratory
Nowzari, CameronGeorge Mason University
Keywords: Event-based control, Multi-agent systems, Control under communication constraints
Abstract: Agreeing on a common time is essential to many coordinated tasks in wireless networks, but this is difficult to accomplish when each agent only has access to a local hardware clock. Therefore, clock synchronization is essential in order to carry out many of these coordinated tasks. While there are numerous existing algorithms for clock synchronization, many result in disruptive discontinuous virtual clocks, and most rely on regular communication, which does not scale with large systems. Instead, this paper presents a novel clock synchronization algorithm, which allows for a continuous virtual clock, with a dynamic event-triggered communication strategy which is strongly non-Zeno because it guarantees a designable positive time between communication instances.
Paper VI115-07.8 
PDF · Video · Event-Based Collision Avoidance Utilising a Channel Estimation Method

Schwung, MichaelRuhr-Universität Bochum
Roth, StefanRuhr University Bochum
Karacora, YaseminRuhr University Bochum
Lunze, JanRuhr-Universität Bochum
Sezgin, AydinRuhr-University Bochum
Keywords: Control under communication constraints, Event-based control, Multi-agent systems
Abstract: This paper proposes a networked event-based method for collision avoidance of moving objects in a leader-follower structure. It extends the results of a previous paper to cope with communication constraints from an information theoretical perspective. The objects are locally controlled and connected by a communication network, in which transmission delays and packet losses occur. In the considered setting, the leader can freely change its trajectory while the follower has to avoid collisions by predicting the leader movement, invoking communication at event times that indicate a large uncertainty of the prediction result and adapting its trajectory appropriately. The current properties of the network are determined at each event time by a channel estimation method and are taken into account when generating events and planning the trajectory. In contrast to the existing literature, trajectories are adapted online where the collision-free movement is guaranteed despite of the limited communication by considering the network effects. A simulation study with two quadrotors shows that collisions can only be avoided if the results of the channel estimation are considered.
Paper VI115-07.9 
PDF · Video · Self-Triggered Finite Time Pursuit Strategy for a Two-Player Game

Mulla, AmeerIndian Institute of Technology Dharwad
Keywords: Control under communication constraints, Event-based control, Reachability analysis, verification and abstraction of hybrid systems
Abstract: A continuous-time two player pursuit-evasion game is considered. The players have double-integrator dynamics with bounded acceleration inputs. Unlike, conventional pursuit strategies, it is assumed that, the pursuer does not have continuous access to the states of the players. In this paper, we propose a self-triggered pursuit strategy, in which, the pursuer can choose when the state-information needs to be updated next. The proposed strategy is based on the time-optimal pursuit strategy for a game in which state information is available continuously to both the players. When the bound on acceleration of the evader is smaller than that of the pursuer, the proposed strategy guarantees capture in finite time, with finite number of information updates.
Paper VI115-07.10 
PDF · Video · Event-Triggered Control for Switched Systems in Network Environments

Ma, LangShanghai University
Wang, Yu-LongShanghai University
Han, Qing-LongSwinburne University of Technology
Peng, ChenShanghai University
Keywords: Control under communication constraints, Event-based control, Stability and stabilization of hybrid systems
Abstract: This paper is concerned with event-triggered control for a switched system in network environments. Firstly, a novel event-triggering communication scheme with switching features is proposed. The switching features are taken into full consideration to guarantee the current sampled data to be transmitted if a switch occurs between the last sampling instant and the current sampling instant. The newly proposed event-triggering scheme is advantageous in dealing with switched networked control systems. Secondly, under the event-triggering scheme, an asynchronously switched time-delay system model is established by taking into account effects of network-induced delays. Finally, a mode-dependent state feedback controller gain and event generator parameters co-design method is proposed for the asynchronously switched time-delay system. System performance analysis demonstrates the effectiveness of the proposed methods.
Paper VI115-07.11 
PDF · Video · Distributed Consensus Control for General Linear Multi-Agent Systems Via a Dynamic Event-Triggered Strategy

Li, YifeiBeijing Institute of Technology
Liu, XiangdongSchool of Automation, 231 Staff, Beijing Institute OfTechnology
Du, ChangkunBeijing Institute of Technology
Liu, HaikuoBeijing Institute of Technology
Lu, PingliBeijing Institute of Technology
Keywords: Multi-agent systems, Event-based control, Consensus
Abstract: This paper puts forward a distributed dynamic event-triggered strategy to solve the distributed event-triggered consensus problem of linear multi-agent systems under directed graphs. Based on dynamic triggering function, each agent can reach consensus asymptotically. Different from existing static triggering schemes, the proposed dynamic triggering scheme, where an internal dynamic variable is involved, results in larger inter-event times and also leads to less communication overheads among agents, which is conducive to guaranteeing that Zeno behavior is excluded for each agent. In addition, under the proposed strategy, neither controller updates nor triggering threshold detections require continuous communication. Finally, the effectiveness of the theoretical analysis is demonstrated by numerical simulations.
Paper VI115-07.12 
PDF · Video · Event-Triggered Consensus for Euler-Lagrange Systems with Communication Delay

Budde genannt Dohmann, PabloTechnical Unviversity of Munich
Hirche, SandraTechnical University of Munich
Keywords: Networked robotic systems, Event-based control
Abstract: Distributed cooperative control of multi-agent systems, typically requires some form of information exchange in order to achieve coordination between individual agents. Especially in wireless communication systems, communication delays can lead to instability and can not be neglected during the design of the control law. We propose a novel coordination scheme for Euler-Lagrange systems taking into account constant communication delays. Additionally, in order to save limited resources we propose an event-triggered strategy for the communication between agents, as well as for th