Systems Theory and Automatic Control

Publications

Books and Volumes

[17] D. Hast. Structured Design of Parametric Fault Candidates: A set-based Approach. . 2019.
[16] N. Rudolph. Set-based Multi-scale Modeling and Analysis of Signal Transduction Pathways. PhD thesis, Otto-von-Guericke University Magdeburg, 2019.
[15] A. Savchenko. Efficient Set-based Process Monitoring and Fault Diagnosis. PhD thesis, Otto-von-Guericke University Magdeburg, 2017.
[14] J. Zometa. Code generation for model predictive control of embedded systems. 2017.
[13] L. Carius. Control and Model-Based Analysis of Microaerobic Processes using Rhodospirillum rubrum as Model Organism. Number 6 in Contributions in Systems Theory and Automatic Control, Otto-von-Guericke Universität Magdeburg. 2016.
[12] K. Kazim. Towards a unified approach for path-following and force-feedback using nonlinear model predictive control. 2016.
[11] B. Huang, R. Findeisen, and B. Guay, M. Gopaluni. 9th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM), volume 48. June 2015.
[10] P. Rumschinski. Verification of system properties of polynomial systems using discrete-time approximations and set-based analysis. 2015.
[9] P. Varutti. Model Predictive Control for Nonlinear Networked Control Systems, A Model-based Compensation Approach for Nondeterministic Communication Networks. Number 5 in Contributions in Systems Theory and Automatic Control, Otto-von-Guericke Universität Magdeburg. June 2014.
[8] P. Benner, R. Findeisen, D. Flockerzi, U. Reichl, and K. Sundmacher. Large-Scale Networks in Engineering and Life Sciences. Modeling and Simulation in Science, Engineering and Technology. 2014.
[7] S. Borchers. Set-membership Estimation, Analysis, and Design of Experiments for Biological Processes. Number 4 in Contributions in Systems Theory and Automatic Control, Otto-von-Guericke Universität Magdeburg. August 2013.
[6] T. Faulwasser. Optimization-Based Solutions to Constrained Trajectory-Tracking and Path-Following Problems. Number 3 in Contributions in Systems Theory and Automatic Control, Otto-von-Guericke Universität Magdeburg. February 2013.
[5] L. Grüne, F. Allgöwer, R. Findeisen, J. Fischer, D. Groß, U. Hanebeck, M. Müller, J. Pannek, M. Reble, O. Stursberg, P. Varutti, K. Worthmann, and B. Kern. Distributed and Networked Model Predictive Control in Control Theory of Digitally Networked Systems. 2013.
[4] S. Maldonado. Force-induced Bone Adaptation: A Systems Biology Perspective Towards Therapy Design. Number 2 in Contributions in Systems Theory and Automatic Control, Otto-von-Guericke Universität Magdeburg. February 2012.
[3] S. Streif. Understanding Phototaxis of Halobacterium salinarum: A Systems Biology Approach. Number 1 in Contributions in Systems Theory and Automatic Control, Otto-von-Guericke Universität Magdeburg. May 2011.
[2] R. Findeisen, L. Biegler, and F. Allgöwer, editors. Assessment and Future Directions of Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences. 2008.
[1] R. Findeisen. Nonlinear Model Predictive Control: A Sampled-Data Feedback Perspective. 2006.

Journals Articles and Book Chapters (all peer reviewed)

[105] J. Bethge, R. Findeisen, D. Le, M. Merkert, H. Rewald, S. Sager, A. Savchenko, and S. Sorgatz. Mathematical optimization and machine learning for efficient urban traffic. submitted to KoMSO German Success Stories in Industrial Mathematics, 2020. submitted.
[104] M. Maiworm, D. Limón, and R. Findeisen. Online learning-based model predictive control with Gaussian process models and stability guarantees. International Journal of Robust and Nonlinear Control, 2020. (submitted).
[103] G. Hernandez-Mejia, A. Y. Alanis, M. Hernandez-Gonzalez, R. Findeisen, and E. A. Hernandez-Vargas. Passivity-Based Inverse Optimal Impulsive Control for Influenza Treatment in the Host. IEEE Transactions on Control Systems Technology, 2019.
[102] H. Lindhorst, S. Lucia, R. Findeisen, and S. Waldherr. Modeling enzyme controlled metabolic networks in rapidly changing environments by robust optimization. IEEE Control Systems Letters, 3(2):248--253, 2019.
[101] Mešanović, X. Wu, S. Schuler, U. Münz, F. Dörfler, and R. Findeisen. Optimal Design of Distributed Controllers for Large-Scale Cyber-Physical Systems. Design Automation of Cyber-Physical Systems, 2019.
[100] H. Reeh, N. Rudolph, U. Billing, H. Christen, S. Streif, E. Bullinger, M. Schliemann-Bullinger, R. Findeisen, F. Schaper, H. J. Huber, and A. Dittrich. Response to IL-6 trans- and IL-6 classic signalling is determined by the ratio of the IL-6 receptor α to gp130 expression: Fusing experimental insights and dynamic modelling. Cell communication and signaling - London: Biomed Central, 17:46, 2019.
[99] C. Wagner, M. F. Green, M. Maiworm, P. Leinen, T. Esat, N. Ferri, N. Friedrich, R. Findeisen, A. Tkatchenko, R. Temirov, and F. S. Tautz. Quantitative imaging of electric surface potentials with single-atom sensitivity. Nature materials, 2019.
[98] R. Findeisen, K. Graichen, and M. Mönnigmann. Eingebettete Optimierung in der Regelungstechnik--Grundlagen und Herausforderungen. at-Automatisierungstechnik, 66(11):877--902, 2018.
[97] B. Jabarivelisdeh and S. Waldherr. Optimization of bioprocess productivity based on metabolic-genetic network models with bilevel dynamic programming. Biotechnology and Bioengineering, 115:1829 -- 1841, 2018.
[96] U. Krewer, F. Röder, E. Harinath, R. D. Braatz, B. Bedürftig, and R. Findeisen. Dynamic models of li-ion batteries for diagnosis and operation: A review and perspective. Journal of The Electrochemical Society, 165(16):A3656--A3673, 2018.
[95] S. Lucia, D. Navarro, O. Lucía, P. Zometa, and R. Findeisen. Optimized FPGA implementation of model predictive control for embedded systems using high-level synthesis tool. IEEE transactions on industrial informatics, 14(1):137--145, 2018.
[94] J. Matschek, T. Bäthge, T. Faulwasser, and R. Findeisen. Nonlinear Predictive Control for Trajectory Tracking and Path Following: An Introduction and Perspective. Handbook of Model Predictive Control, pages 169--198, 2018.
[93] T. Mühlpfordt, R. Findeisen, V. Hagenmeyer, and T. Faulwasser. Comments on truncation errors for polynomial chaos expansions. IEEE Control Systems Letters, 2(1):169--174, 2018.
[92] M. Oldenburger, B. Bedürftig, A. Gruhle, F. Grimsmann, E. Richter, R. Findeisen, and A. Hintennach. Investigation of the low frequency warburg impedance of li-ion cells by frequency domain measurements. Journal of Energy Storage, 21:272--280, 12 2018.
[91] J. A. Paulson, S. Streif, R. Findeisen, R. D. Braatz, and A. Mesbah. Fast stochastic model predictive control of end-to-end continuous pharmaceutical manufacturing. In Comput. Aided Chemical Eng., volume 41, pages 353--378. 2018.
[90] T. Faulwasser, T. Weber, P. Zometa, and R. Findeisen. Implementation of nonlinear model predictive path-following control for an industrial robot. IEEE Transactions on Control Systems Technology, 25(4):1505--1511, 2017.
[89] C. Kankeu, K. Clarke, E. Passante, and H. Huber. Doxorubicin-induced chronic dilated cardiomyopathy—the apoptosis hypothesis revisited. Journal of Molecular Medicine, pages 1--10, 2017.
[88] M. Kishida, M. Kögel, and R. Findeisen. Combined event- and self-triggered control approach with guaranteed finite-gain L2 stability for uncertain linear systems. IET Control Theory Applications, 11(11):1674--1683, 2017.
[87] S. Lucia, D. Navarro, O. Lucia, P. Zometa, and R. Findeisen. Optimized FPGA implementation of model predictive control for embedded systems using high level synthesis tool. IEEE Transactions on Industrial Informatics, 2017.
[86] J. Matschek, E. Bullinger, F. von Haeseler, M. Skalej, and R. Findeisen. Mathematical 3d modelling and sensitivity analysis of multipolar radiofrequency ablation in the spine. Mathematical biosciences, 284:51--60, 2017.
[85] M. Oldenburger, B. Bedürftig, A. Gruhle, and E. Richter. A new approach to measure the non-linear butler-volmer behavior of electrochemical systems in the time domain. Journal of Energy Storage, 14:16--21, 11 2017.
[84] D. Veltman, T. Laeremans, E. Passante, and H. Huber. Signal transduction analysis of the NLRP3-inflammasome pathway after cellular damage and its paracrine regulation. Journal of theoretical biology, 415:125--136, 2017.
[83] A. Šatrauskienė, R. Navickas, A. Laucevičius, and H. Huber. Identifying differential mir and gene consensus patterns in peripheral blood of patients with cardiovascular diseases from literature data. BMC cardiovascular disorders, 17(1):173, 2017.
[82] T. Faulwasser and R. Findeisen. Nonlinear model predictive control for constrained output path following. IEEE Transactions on Automatic Control, 61(4):1026--1039, 2016.
[81] M. Schliemann-Bullinger, D. Fey, T. Bastogne, R. Findeisen, P. Scheurich, and E. Bullinger. The experimental side of parameter estimation. In L. Geris and D. Gomez-Cabrero, editors, Uncertainty in Biology - A Computational Modeling Approach, volume 17 of Studies in Mechanobiology, Tissue Engineering and Biomaterials. 1 edition, 2016.
[80] S. Streif, K. Kim, P. Rumschinski, M. Kishida, D. Shen, R. Findeisen, and R. D. Braatz. Robustness analysis, prediction and estimation for uncertain biochemical networks: An overview. Journal of Process Control, 42:14--34, 2016.
[79] S. Koulchitsky, C. Delairesse, T. Beeken, A. Monteforte, J. Dethier, E. Quertemont, R. Findeisen, E. Bullinger, and V. Seutin. Activation of D2 autoreceptors alters cocaine-induced locomotion and slows down local field oscillations in the rat ventral tegmental area. Neuropharmacology, 108:120--127, 2016.
[78] S. Lucia, M. Kögel, P. Zometa, D. E. Quevedo, and R. Findeisen. Predictive control, embedded cyberphysical systems and systems of systems - a perspective. Annual Reviews in Control, 41:193 -- 207, 2016.
[77] D. Hast, S. Streif, and R. Findeisen. Detection and isolation of parametric faults in hydraulic pumps using a set-based approach and quantitative-qualitative fault specifications. Control Engineering Practice, 40:61--70, 2015.
[76] J.-B. Tylcz, T. Bastogne, H. Benachour, D. Bechet, E. Bullinger, H. Garnier, and M. Barberi-Heyob. A model-based pharmacokinetics characterization method of engineered nanoparticles for pilot studies. IEEE Transactions on NanoBioscience, 14(4):368--377, 6 2015.
[75] S. Blacher, C. Erpicum, B. Lenoir, J. Paupert, G. Moraes, S. Ormenese, E. Bullinger, and A. Noël. Cell invasion in the spheroid sprouting assay: A spatial organisation analysis adaptable to cell behaviour. PLoS ONE, 9(5):e97019, 05 2014.
[74] L. Carius, P. Rumschinski, T. Faulwasser, D. Flockerzi, H. Grammel, and R. Findeisen. Model-based derivation, analysis and control of unstable microaerobic steady-states—considering rhodospirillum rubrum as an example. Biotechnology and bioengineering, 111(4):734--747, 2014.
[73] T. Faulwasser, V. Hagenmeyer, and R. Findeisen. Constrained reachability and trajectory generation for flat systems. Automatica, 50(4):1151--1159, 2014.
[72] M. Kishida, P. Rumschinski, R. Findeisen, and R. Braatz. Efficient Polynomial-time Outer Bounds on State Trajectories for Uncertain Polynomial Systems using Skewed Structured Singular Values. IEEE Transactions on Automatic Control, 59(11):3063 -- 3068, 2014.
[71] M. Rausch, R. Klein, S. Streif, and R. Findeisen. Modellbasierte Zustandsschätzung für Lithium-Ionen-Batterien (engl. Model-based state estimation for lithium-ion batteries). AT- Automatisierungstechnik, 62(4):296--311, 2014.
[70] J. K. Scott, R. Findeisen, R. Braatz, and D. Raimondo. Input Design for Guaranteed Fault Diagnosis Using Zonotopes. Automatica, 50(6):1580--1589, 2014.
[69] L. Carius, A. B. Carius, M. McIntosh, and H. Grammel. Quorum sensing influences growth and photosynthetic membrane production in high-cell-density cultivations of rhodospirillum rubrum. BMC microbiology, 13(1):189, 2013.
[68] L. Carius, O. Hädicke, and H. Grammel. Stepwise reduction of the culture redox potential allows the analysis of microaerobic metabolism and photosynthetic membrane synthesis in rhodospirillum rubrum. Biotechnology and bioengineering, 110(2):573--585, 2013.
[67] I. Alvarado, R. Findeisen, P. Kühl, F. Allgöwer, and D. Limón. Iteratively Improving Moving Horizon Observers for Repetitive Processes. In F. Lamnabhi-Lagarrigue, S. Laghrouche, A. Loria, and E. Panteley, editors, Taming Heterogeneity and Complexity Embedded Control, pages 39--54, 2013.
[66] S. Borchers, S. Freund, A. Rath, S. Streif, U. Reichl, and R. Findeisen. Identification of growth phases and influencing factors in cultivation of AGE1.HN cells using set-based methods. Plos One, 8(8):11, 2013.
[65] R. Klein, N. Chaturvedi, J. Christensen, J. Ahmed, R. Findeisen, and A. Koji c. Electrochemical Model Based Observer Design for a Lithium-Ion Battery. IEEE J. Cont. Syst. Techn., 21(2):289--301, 2013.
[64] M. Readman, M. Schliemann, D. Kalamatianos, and E. Bullinger. A feedback Control Perspective on Models of Apoptosis Signal Transduction. Chaos, Solitons & Fractals, 50:93--99, 2013. Special Issue enquoteFunctionality and Dynamics in Biological Systems.
[63] M. Kreysing, R. Pusch, D. Haverkate, M. Landsberger, J. Engelmann, J. Ruiter, C. Mora-Ferrer, E. Ulbricht, J. Grosche, K. Franze, S. Streif, S. Schumacher, F. Makarov, J. Kacza, J. Guck, H. Wolburg, J. Bowmaker, G. von der Emde, S. Schuster, H.-J. Wagner, A. Reichenbach, and M. Francke. Photonic Crystal Light Collectors in Fish Retina Improve Vision in Turbid Water. Science, 336(6089):1700--1703, 2012.
[62] S. Raković, B. Kouvaritakis, R. Findeisen, and M. Cannon. Homothetic Tube Model Predictive Control. Automatica, 48(8):1631--1638, 2012.
[61] M. Cannon, C. Q, B. Kouvaritakis, and S. Raković. Stochastic tube Mpc with state estimation. Automatica, 48(3):536--541, 2012.
[60] S. Raković, B. Kouvaritakis, M. Cannon, C. Panon, and R. Findeisen. Parameterized Tube Model Predictive Control. IEEE Transactions on Automatic Control, 57(11):2746--2761, 2012.
[59] P. Rumschinski, S. Streif, and R. Findeisen. Combining qualitative information and semi-quantitative data for guaranteed invalidation of biochemical network models. Int. J. of Robust and Nonlinear Control, 22(10):1157--1173, 2012.
[58] S. Streif, A. Savchenko, P. Rumschinski, S. Borchers, and R. Findeisen. ADMIT: a toolbox for guaranteed model invalidation, estimation, and qualitative-quantitative modeling. Bioinformatics, 28(9):1290--1291, 2012.
[57] M. Ma, H. Chen, R. Findeisen, and F. Allgöwer. Nonlinear receding horizon control of quadruple-tank system and real-time implementation. International Journal of Innovative Computing, Information and Control, 8(10(B)):7083--7093, 2012.
[56] L. Trotta, E. Bullinger, and R. Sepulchre. Global analysis of dynamical decision-making models through local computation around the hidden saddle. PLoS ONE, 7(3):e33110, 2012.
[55] S. Waldherr, S. Streif, and F. Allgöwer. Design of biomolecular network modifications for adaptation. IET Syst Biol., 6(6):223--231, 2012.
[54] S. Borchers, S. Bosio, R. Findeisen, U. Haus, P. Rumschinski, and R. Weismantel. Graph problems arising from parameter identification of discrete dynamical systems. Mathematical Methods of Operations Research, 73(3):381--400, 2011.
[53] M. Cannon, B. Kouvaritakis, S. Raković, and Q. Cheng. Stochastic tubes in model predictive control with probabilistic constraints. IEEE Transactions on Automatic Control, 56(1):194--200, 2011.
[52] M. Cannon, B. Kouvaritakis, J. Buerger, and S. Raković. Robust tubes in nonlinear model predictive control. IEEE Transactions on Automatic Control, 56(8):1942--1947, 2011.
[51] M. Schliemann, E. Bullinger, S. Borchers, F. Allgöwer, R. Findeisen, and P. Scheurich. Heterogeneity reduces sensitivity of cell death for TNF-stimuli. BMC Systems Biology, 5(204):28, 2011.
[50] C. Böhm, R. Findeisen, and F. Allgöwer. Robust Control of Constrained Sector Bounded Lure Systems with Applications to Nonlinear Model Predictive Control. J. on Dynamics of Continuous, Discrete and Impulsive Systems, Series B: Applications and Algorithms, 17(6):935--958, 2010.
[49] N. Chaturvedi, R. Klein, J. Christensen, J. Ahmed, and A. Kojić. Algorithms for Advanced Battery-Management Systems. Control Systems Magazine, IEEE, 30(3):49--68, 2010.
[48] Z. Artstein and S. Raković. Set Invariance Under Output Feedback: A set-Dynamics Approach. Int. J. of Systems Science, 42(4):539--555, 2010.
[47] B. Kouvaritakis, M. Cannon, S. Raković, and C. Q. Explicit use of probabilistic distributions in linear predictive control. Automatica, 46(10):1719--1724, 2010.
[46] P. Rumschinski, S. Borchers, S. Bosio, R. Weismantel, and R. Findeisen. Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks. BMC systems biology, 4:69, 2010.
[45] J. Hasenauer, P. Rumschinski, S. Waldherr, S. Borchers, F. Allgöwer, and R. Findeisen. Guaranteed Steady State Bounds for Uncertain (Bio-)Chemical Processes using Infeasibility Certificates. J.Proc.Contr., 20(9):1076--1083, 2010.
[44] L. Zeiger and H. Grammel. Model-based high cell density cultivation of rhodospirillum rubrum under respiratory dark conditions. Biotechnology and bioengineering, 105(4):729--739, 2010.
[43] C. Böhm, F. Heß, R. Findeisen, and F. Allgöwer. An NmpC approach to avoid weakly observable trajectories. In L. Magni, D. Raimundo, and F. Allgöwer, editors, Nonlinear Model Predictive Control: Towards New Challenging Applications, Lecture Notes in Control and Inform. Sciences LNCIS 384, pages 275--284, 2009.
[42] T. Faulwasser and R. Findeisen. Nonlinear model predictive path-following control. In L. Magni, D. Raimundo, and F. Allgöwer, editors, Nonlinear Model Predictive Control: Towards New Challenging Applications, Lecture Notes in Control and Inform. Sciences LNCIS 384, pages 335--343, 2009.
[41] T. Faulwasser, B. Kern, P. Varutti, and R. Findeisen. Prädiktive Regelung nichtlinearer Systeme unter asynchronen Mess- und Stellsignalen. At-Automatisierungstechnik, 57(6):279--286, 2009.
[40] T. Faulwasser and R. Findeisen. Ein prädiktiver Ansatz zur Lösung nichtlinearer Pfadverfolgungsprobleme unter Beschränkungen. Automatisierungstechnik, 57(8):386--394, 2009.
[39] D. Fey, R. Findeisen, and E. Bullinger. Identification of biochemical reaction networks using a parameter-free coordinate system. Control Theory in Systems Biology, MIT press, pages 293--310, 2009.
[38] D. Mayne, S. Raković, R. Findeisen, and F. Allgöwer. Robust output feedback model predictive control of constrained linear systems: time-varying case. Automatica, 45(9):2082--2087, 2009.
[37] S. Raković. Set theoretic methods in model predictive control. In L. Magni, D. Raimundo, and F. Allgöwer, editors, Nonlinear Model Predictive Control: Towards New Challenging Applications, Lecture Notes in Control and Inform. Sciences LNCIS 384, pages 41--54, 2009.
[36] S. Raković and B. Miroslav. Local Control Lyapunov Functions for Constrained Linear Discrete Time Systems: The Minkowski Algebra Approach. IEEE Transactions on Automatic Control, 54(11):2686--2692, 2009.
[35] S. Streif, S. Waldherr, F. Allgöwer, and R. Findeisen. Steady state sensitivity analysis of biochemical reaction networks: A brief review and new methods. In A. Jayaraman and J. Hahn, editors, Syst. Anal. Biological Networks, Methods in Bioengineering, pages 129--148. 2009.
[34] R. Findeisen and P. Varutti. Stabilizing nonlinear predictive control over nondeterministic communication networks. In L. Magni, D. Raimundo, and F. Allgöwer, editors, Nonlinear Model Predictive Control: Towards New Challenging Applications, Lecture Notes in Control and Inform. Sciences LNCIS 384, pages 167--179, 2009.
[33] B. Kern, C. Böhm, R. Findeisen, and F. Allgöwer. Receding horizon control for linear periodic time-varying systems subject to input constraints. In L. Magni, D. Raimundo, and F. Allgöwer, editors, Nonlinear Model Predictive Control: Towards New Challenging Applications, Lecture Notes in Control and Inform. Sciences LNCIS 384, pages 109--117, 2009.
[32] E. Bullinger, D. Fey, M. Farina, and R. Findeisen. Identifikation biochemischer Reaktionsnetzwerke: Ein beobachterbasierter Ansatz. AT-Automatisierungstechnik, 56(5):269--279, 2008.
[31] M. Gurumurthy, C. H. Tan, R. Ng, L. Zeiger, J. Lau, J. Lee, A. Dey, R. Philp, Q. Li, T. M. Lim, et al. Nucleophosmin interacts with hexim1 and regulates rna polymerase ii transcription. Journal of molecular biology, 378(2):302--317, 2008.
[30] A. Graefe, C. Orwat, and T. Faulwasser. Der Umgang mit Barrieren bei der Einführung von Pervasive Computing - Ein Literaturüberblick. Technikfolgenabschätzung Theorie und Praxis, 1:13--19, 2008.
[29] C. Orwat, A. Graefe, and T. Faulwasser. Towards pervasive computing in health care - a literature review. BMC Medical Informatics and Decision Making, 8, 2008.
[28] G. Papavasiliou, P. Songprawat, V. Pérez-Luna, E. Hammes, M. Morris, Y. Chiu, and E. Brey. Three-dimensional pattering of poly (ethylene Glycol) hydrogels through surface-initiated photopolymerization. Tissue Eng. Part C, Methods, 14(2):129--140, 2008.
[27] S. Maldonado, R. Findeisen, and F. Allgöwer. Understanding the process of force induced bone growth and adaptation trough mathematical modelling. Bone, 42(1):61--, 2008.
[26] S. Maldonado, R. Findeisen, and F. Allgöwer. Describing force-induced bone growth and adaptation by a mathematical model. J. of Musculoskeletal and Neuronal Interactions, 8(1):15--17, 2008.
[25] E. Bullinger, R. Findeisen, D. Kalamatianos, and P. Wellstead. System and control theory furthers the understanding of biological signal transduction. In I. Queinnec, S. Tarbouriech, G. Garcia, and S.-I. Niculescu, editors, Biology and Control Theory: Current Challenges, volume 357 of Lecture Notes in Control and Information Sciences, pages 123--135. 2007.
[24] H. Chen, X. Gao, H. Wang, and R. Findeisen. On disturbance attenuation of nonlinear moving horizon control. In R. Findeisen, L. Biegler, and F. Allgöwer, editors, Assessment and Future Directions Nonlinear Model Predictive Control, Lecture Notes in Control and Information Sciences, pages 283--294, 2007.
[23] M. Diehl, R. Findeisen, and F. Allgöwer. A stabilizing Real-time Implementation of Nonlinear Model Predictive Control. In L. Biegler, O. Ghattas, M. Heinkenschloss, D. Keyes, and B. van Bloem Wanders, editors, Real-Time PDE Optimization, pages 25--48, 2007.
[22] R. Findeisen, T. Raff, and F. Allgöwer. Sampled-data Nonlinear Model Predictive Control for Constrained Continuous Time Systems. In S. Tarbouriech, G. Garcia, and A. Glattfelder, editors, Advanced Strategies in Control Syst. with Input and Output Constraints, Lecture Notes in Control and Information Sciences, pages 207--235, 2007.
[21] R. Lepore, A. Vande Wouwer, M. Remy, R. Findeisen, Z. Nagy, and F. Allgöwer. Optimization Strategies for a Mma Polymerization Reactor. Comp.& Chem.Eng., 31(4):281--291, 2007.
[20] T. Raff, R. Findeisen, M. Herceg, and F. Allgöwer. Nonlinear Model Predictive Control of a Turbocharged Diesel Engine. In F. Allgöwer, L. Del Re, M. Diehl, and R. Scattolini, editors, Predictive Control Combustion Engines. 2007.
[19] D. Mayne, S. Raković, R. Findeisen, and F. Allgöwer. Robust output feedback model predictive control of constrained linear systems. Automatica, 1217-1222(42):7, 2006.
[18] T. Raff, C. Ebenbauer, R. Findeisen, and F. Allgöwer. Nonlinear model predictive control and sum of squares techniques. In M. Diehl and K. Mombauer, editors, Fast Motions in Biomechanics and Robotics, Lecture Notes in Control and Information Sciences, pages 325--344, 2006.
[17] M. Diehl, R. Findeisen, F. Allgöwer, H. Bock, and J. Schlöder. Nominal stability of the real-time iteration scheme for nonlinear model predictive control. IEE Control Theory Appl., 152(3):296--308, 2005.
[16] T. Raff, C. Ebenbauer, R. Findeisen, and F. Allgöwer. Remarks on Moving Horizon State Estimation with Guaranteed Convergence. In T. Meurer, K. Graichen, and E. Gilles, editors, Control and Observer Des. for Nonlinear Finite and Infinite Dimensional Syst., pages 67--80. 2005.
[15] F. Allgöwer, R. Findeisen, and Z. Nagy. Nonlinear Model Predictive Control: From Theory to Application. J. Chin. Inst. Chem. Engrs., 35(3):299--315, 2004.
[14] F. Allgöwer, R. Findeisen, and C. Ebenbauer. Nonlinear Model Predictive Control. Encyclopedia for Life Support Systems (EOLSS) article contribution 6.43.16.2, 2003.
[13] M. Diehl, R. Findeisen, S. Schwarzkopf, I. Uslu, F. Allgöwer, H. Bock, and J. Schl dër. An Efficient Approach for Nonlinear Model Predictive Control of Large-Scale Systems. Part Ii: Experimental Evaluation Considering the Control of a Distillation Column. Automatisierungstechnik, 51(1):22--29, 2003.
[12] R. Findeisen, L. Imsland, F. Allgöwer, and B. Foss. Output Feedback Stabilization for Constrained Systems with Nonlinear Predictive Control. Int.J.of Robust and Nonlinear Control, 13(3-4):211--227, 2003.
[11] R. Findeisen, L. Imsland, F. Allgöwer, and B. Foss. State and Output Feedback Nonlinear Model Predictive Control: An Overview. Europ.J.Contr., 9(2-3):190--206, 2003.
[10] R. Findeisen, L. Imsland, F. Allgöwer, and B. Foss. Towards a Sampled-Data Theory for Nonlinear Model Predictive Control. In C. Kang, M. Xiao, and W. Borges, editors, New Trends in Nonlinear Dynamics and Control, and their Appl., Lecture Notes in Control and Information Sciences, 295, pages 295--313, 2003.
[9] L. Imsland, R. Findeisen, E. Bullinger, F. Allgöwer, and B. Foss. A note on stability, robustness and performance of output feedback nonlinear model predictive control. J. of Proc. Contr., 13(7):633--644, 2003.
[8] L. Imsland, R. Findeisen, F. Allgöwer, and B. Foss. Output feedback stabilization with nonlinear predictive control: Asymptotic properties. J. Modeling Identification and Control, 24(3):169--179, 2003.
[7] M. Diehl, H. Bock, J. Schlöder, R. Findeisen, Z. Nagy, and F. Allgöwer. Real-time optimization and Nonlinear Model Predictive Control of Processes governed by Differential-Algebraic Equations. J.Proc.Contr., 12(4):577--585, 2002.
[6] M. Diehl, R. Findeisen, S. Schwarzkopf, I. Uslu, F. Allgöwer, H. G. Bock, and J. Schl dër. An Efficient Approach for Nonlinear Model Predictive Control of Large-Scale Systems Part I: Description of the Methodology. Automatisierungstechnik, 50(12):557--567, 2002.
[5] R. Findeisen and F. Allgöwer. The Quasi-Infinite Horizon Approach to Nonlinear Model Predictive Control. In A. Zinober and D. Owens, editors, Nonlinear and Adaptive Control, Lecture Notes in Control and Information Sciences, pages 89--105, 2002.
[4] M. Diehl, I. Uslu, R. Findeisen, S. Schwarzkopf, F. Allgöwer, H. Bock, T. B\rner, E. Gilles, A. Kienle, J. Schl "ër, and E. Stein. Real-Time Optimization of Large Scale Process Models: Nonlinear Model Predictive Control of a High Purity Distillation Column. In M. Grötschel, S. Krumke, and J. Rambau, editors, Online Optimization Large Scale Systems: State Art, pages 363--384, 2001.
[3] R. Findeisen and F. Allgöwer. A nonlinear Model Predictive Control Scheme for the Stabilization of Setpoint Families. Journal A, Benelux Quarterly Journal on Automatic Control, 41(1):37--45, 2000.
[2] R. Findeisen and F. Allgöwer. Nonlinear model predictive control for index-one Dae systems. In F. Allgöwer and A. Zheng, editors, Nonlinear Model Predictive Control, volume 26 of Progress in Systems and Control Theory, pages 145--162, 2000.
[1] H. Lindhorst, S. Lucia, R. Findeisen, and S. Waldherr. Modelling metabolic networks including gene expression and uncertainties. arXiv preprint arXiv:1609.08961.

Submitted Journals Articles

[6] T. Hüfner, M. Oldenburger, B. Bedürftig, and A. Gruhle. Lithium diffusion between active area and overhang of graphite anodes as a function of temperature and overhang geometry. 2019. Submitted.
[5] A. Mešanović, U. Münz, A. Szabo, M. Mangold, J. Bamberger, J. Bamberger, M. Matzger, C. Heyde, R. Krebs, and R. Findeisen. Guaranteed H infinity controller parameter tuning for power systems: method and experimental evaluation. 2019.
[4] A. Mešanović, U. Münz, and R. Findeisen. Parameter tuning decentralized structured controllers in large power systems. 2019.
[3] A. Savchenko, P. Rumschinski, S. Streif, and R. Findeisen. Quantizations for process monitoring of biotechnological systems based on diagnosability. To be submitted, 2019.
[2] Y. Wan, D. E. Shen, S. Lucia, R. Findeisen, and R. Braatz. An improved polynomial chaos approach to robust h∞ static output-feedback control. 2019.
[1] D. E. Shen, S. Lucia, Y. Wan, R. Findeisen, and R. D. Braatz. Polynomial chaos-based h2-optimal static output feedback control of systems with probabilistic parametric uncertainties. 2019.

Proceedings (peer reviewed)

[240] M. Ibrahim, M. Kögel, C. Kallies, and R. Findeisen. Contract-based hierarchical model predictive control and planning for autonomous vehicle. In Proc. 21st IFAC World Congress, (IFAC WC 2020), 2020.
[239] M. Ibrahim, C. Kallies, and R. Findeisen. Learning-supported approximated optimal control for autonomous vehicles in the presence of state dependent uncertainties. In Proc. 19st Eur. Control Conference, (ECC 2020), 2020.
[238] M. Kögel and R. Findeisen. Robust MPC with reduced conservatism by blending multiples tubes. In Proc. Amer. Control Conf., 2020. Accepted.
[237] J. Matschek, T. Gonschorek, M. Hanses, N. Elkmann, F. Ortmeier, and R. Findeisen. Learning references with Gaussian processes in model predictive control applied to robot assisted surgery. In Proc. Eur. Control Conf. (ECC), pages 362--367, 2020.
[236] J. Matschek and R. Findeisen. Learning supported Model Predictive Control for Tracking of Periodic References. In Proc. Mach. Learning Res., volume 120, pages 511--520, 2020.
[235] M. Pfefferkorn, M. Maiworm, C. Wagner, F. S. Tautz, and R. Findeisen. Fusing online Gaussian process-based learning and control for scanning quantum dot microscopy. In 59th Cf. Decisi and Ctrol (CDC), 2020. (submitted).
[234] J. Pohlodek, A. Rose, B. Morabito, L. Carius, and R. Findeisen. Data-driven Metabolic Network Reduction for Multiple Modes Considering Uncertain Measurements. In 21st IFAC World Congr., 2020.
[233] B. Morabito, A. Kienle, R. Findeisen, and L. Carius. Multi-mode model predictive control and estimation for uncertain biotechnological processes. In 12th IFAC Symp. Dynamics and Ctrol Process Systems, including Biosystems DYCOPS 2019, volume 52, pages 709 -- 714, July 2019.
[232] P. Andonov, A. Savchenko, and R. Findeisen. Controller Parametrization for Offset-free Control Using Set-Based Feasibility Methods. In Amer. Control Conf. (ACC), pages 2795--2800, 2019.
[231] S. Di Cairano, T. Bäthge, and R. Findeisen. Modular Design for Constrained Control of Actuator-Plant Cascades. In Proc. IEEE Amer. Control Conf. (ACC), pages 1755--1760, 2019.
[230] M. Ibrahim, J. Matschek, B. Morabito, and R. Findeisen. Improved area covering in dynamic environments by nonlinear model predictive path following control. In Proc. 8th IFAC Symp. Mechatric Syst. (MECHATRONICS 2019), 2019.
[229] M. Ibrahim, J. Matschek, B. Morabito, and R. Findeisen. Hierarchical model predictive control for autonomous vehicle area coverage. In Proc. 21st IFAC Symp. Automat. Ctrol in Aerospace (ACA 2019), 2019.
[228] M. Kögel and R. Findeisen. Combined online communication scheduling and output feedback MPC of cyber-physical systems. In Proc. IEEE Annu. Consumer Commun. Networking Conf., 2019.
[227] M. Kögel, D. E. Quevedo, and R. Findeisen. Combined control and communication scheduling for constrained systems using robust output feedback MPC. In Proc. Eur. Control Conf., pages 1778--1783, 2019.
[226] S. H. Mousavi, N. Noroozi, R. Geiselhart, M. Kögel, and R. Findeisen. On integral input-to-state stability of event-triggered control systems. In Proc. IEEE Conf. Decision and Control, pages 1674--1679, 2019.
[225] S. Blasi, M. Kögel, and R. Findeisen. Distributed model predictive control using cooperative contract options. In Proc. 6th Nonlinear Model Predictive Control Conf. (NMPC), pages 544--550, August 2018.
[224] L. Carius, J. Pohlodek, B. Morabito, A. Franz, M. Mangold, R. Findeisen, and A. Kienle. Model-based state estimation utilizing a hybrid cybernetic model. In Advanced Control Chemical Processes (AdChem), July 2018.
[223] A. Mešanović, D. Unseld, U. Münz, C. Ebenbauer, and R. Findeisen. Parameter Tuning and Optimal Design of Decentralized Structured Controllers for Power Oscillation Damping in Electrical Networks. In 2018 Annu. Amer. Control Conf. (ACC), pages 3828--3833, June 2018.
[222] P. Andonov, B. Morabito, A. Savchenko, and R. Findeisen. Admissible Control Parametrization of Uncertain Finite-time Processes With Application to Li-ion Battery Management. In Eur. Control Conf. (ECC), pages 2338--2343, 2018.
[221] T. Bäthge, M. Kögel, S. Di Cairano, and R. Findeisen. Contract-based Predictive Control for Modularity in Hierarchical Systems. In Proc. 6th IFAC Cf. Nlinear Model Predictive Ctrol (NMPC), pages 598--603, 2018.
[220] J. Bethge, B. Morabito, J. Matschek, and R. Findeisen. Multi-mode learning supported model predictive control with guarantees. In Proc. 6th Nonlinear Model Predictive Control Conf. (NMPC), pages 616--621, 2018.
[219] K. J. Kazim, J. Bethge, J. Matschek, and R. Findeisen. Combined predictive path following and admittance control. In Proc. Amer. Control Conf. (ACC), pages 3153--3158, 2018.
[218] M. Kögel, P. Andonov, M. Filax, F. Ortmeier, and R. Findeisen. Predictive Tracking Control of a Camera - Head Mounted Display System subject to Communication Constraints. In Proc. Eur. Control Conf., pages 1035--1041, 2018.
[217] M. Kögel and R. Findeisen. Improved robust decentralized MPC. In Proc. Eur. Control Conf., pages 312--318, 2018.
[216] M. Kögel and R. Findeisen. Towards Optimal Tuning of Robust Output Feedback MPC. In Proc. IFAC Nonlinear Model Predictive Control Conf., pages 134--140, 2018.
[215] M. Kögel and R. Findeisen. Low latency output feedback predictive control based on optimality conditions. PAMM, 18(1):e201800429, 2018.
[214] M. Maiworm, C. Wagner, R. Temirov, F. S. Tautz, and R. Findeisen. Two-degree-of-freedom control combining machine learning and extremum seeking for fast scanning quantum dot microscopy. In Amer. Control Conf. (ACC), pages 4360--4366, 2018.
[213] M. Maiworm, D. Limón, J. M. Manzano, and R. Findeisen. Stability of Gaussian process learning based output feedback model predictive control. In 6th IFAC Cf. Nlinear Model Predictive Ctrol (NMPC), pages 551--557, 2018.
[212] A. Mešanović, U. Münz, J. Bamberger, and R. Findeisen. Controller Tuning for the Improvement of Dynamic Security in Power Systems. In 2018 IEEE PES Innovative Smart Grid Technologies Conf. Europe (ISGT-Europe), pages 1--6. IEEE, 2018.
[211] A. Mešanović, U. Münz, and R. Findeisen. Coordinated tuning of controller parameters in Ac/Dc grids for power oscillation damping. In 2018 IEEE/PES Transmission and Distribution Conf. and Exposition (T&D), pages 1--9. IEEE, 2018.
[210] A. Mešanović, D. Unseld, U. Münz, C. Ebenbauer, and R. Findeisen. Parameter tuning and optimal design of decentralized structured controllers for power oscillation damping in electrical networks. In 2018 Annu. Amer. Control Conf. (ACC), pages 3828--3833. IEEE, 2018.
[209] M. Naber, F. von Haeseler, N. Rudolph, H. J. Huber, and R. Findeisen. Effects of noisy biological data on picard iteration-based parameter estimation for a glutamate excitotoxicity pathway. In Int. Cf. Foundatis Syst. Biology in Eng. (FOSBE), pages 64--67, 2018.
[208] H. H. Nguyen, A. Savchenko, S. Yu, and R. Findeisen. Improved robust predictive control for lure systems using set-based learning. In Proc. 6th Nonlinear Model Predictive Control Conf. (NMPC), pages 487--492, 2018.
[207] N. Rudolph, P. Andonov, H. J. Huber, and R. Findeisen. Model-supported patient stratification using set-based estimation methods. In Int. Symp. Advanced Ctrol Chemical Processes (AdChem), pages 892--897, 2018.
[206] F. von Haeseler, N. Rudolph, R. Findeisen, and H. J. Huber. Parameter estimation for signal transduction networks from experimental time series using picard iteration. In Advanced Control Chemical Processes (AdChem), pages 191--196, 2018.
[205] Y. Wan, D. E. Shen, S. Lucia, R. Findeisen, and R. D. Braatz. Robust static h-infinity output-feedback control using polynomial chaos. In 2018 Annu. Amer. Control Conf. (ACC), pages 6804--6809. IEEE, 2018.
[204] B. Morabito, R. Klein, and R. Findeisen. Real time feasibility and performance of moving horizon estimation for li-ion batteries based on first principles electrochemical models. In 2017 Amer. Control Conf. (ACC), pages 3457--3462, May 2017.
[203] M. Kishida, M. Kögel, and R. Findeisen. Event-triggered actuator signal update using self-triggered sampled data for uncertain linear systems. In Proc. Amer. Control Conf., pages 3035--3041, 2017.
[202] M. Kögel and R. Findeisen. Robust output feedback MPC for uncertain linear systems with reduced conservatism. In Proc. IFAC World Congr., pages 10685--10690, 2017.
[201] M. Kögel and R. Findeisen. Low latency output feedback model predictive control for constrained linear systems. In Proc. IEEE Conf. Decision and Control, pages 1925--1932, 2017.
[200] M. Kopf, E. Bullinger, H.-G. Giesseler, S. Adden, and R. Findeisen. Model Predictive Control for Aircraft Load Alleviation: Opportunities and Challenges. In Proc.Amer. Control Conf. (ACC), Milwaukee, WI, USA, 27--29 June 2018, 2017.
[199] S. Lucia, L. Carius, and R. Findeisen. Adaptive nonlinear predictive control and estimation of microaerobic processes. IFAC-PapersOnLine, 50(1):12635 -- 12640, 2017. 20th IFAC World Congress.
[198] S. Lucia, J. A. Paulson, R. Findeisen, and R. D. Braatz. On stability of stochastic linear systems via polynomial chaos expansions. In Amer. Control Conf. (ACC), 2017, pages 5089--5094. IEEE, 2017.
[197] S. Lucia, M. Torchio, D. M. Raimondo, R. Klein, R. D. Braatz, and R. Findeisen. Towards adaptive health-aware charging of li-ion batteries: A real-time predictive control approach using first-principles models. In Amer. Control Conf. (ACC), 2017, pages 4717--4722. IEEE, 2017.
[196] J. Matschek, J. Bethge, P. Zometa, and R. Findeisen. Force feedback and path following using predictive control: Concept and application to a lightweight robot. In Proc. 19th IFAC World Congr. Toulouse, pages 10243--10248, 2017.
[195] A. Mešanović, U. Münz, and R. Findeisen. Coordinated tuning of synchronous generator controllers for power oscillation damping. In 2017 IEEE PES Innovative Smart Grid Technologies Conf. Europe (ISGT-Europe), pages 1--6, Sep. 2017.
[194] A. Savchenko, P. Andonov, P. Rumschinski, and R. Findeisen. Multi-objective complexity reduction for set-based fault diagnosis. In Advanced Ctrol Ind. Processes (AdCONIP), 2017 6th Int. Symp., pages 589--594. IEEE, 2017.
[193] D. E. Shen, S. Lucia, Y. Wan, R. Findeisen, and R. D. Braatz. Polynomial chaos-based H2-optimal static output feedback control of systems with probabilistic parametric uncertainties. IFAC-PapersOnLine, 50(1):3536 -- 3541, 2017. 20th IFAC World Congress.
[192] T. Bäthge, S. Lucia, and R. Findeisen. Exploiting Models of Different Granularity in Robust Predictive Control. In Proc. 55th IEEE Cf. Decisi and Ctrol (CDC), pages 2763--2768, 2016.
[191] L. Carius and R. Findeisen. The impact of experimental data quality on computational systems biology and engineering. IFAC-PapersOnLine - Proceedings of the 6th Foundations of Systems Biology in Engineering FOSBE, 49(26):140--146, 2016.
[190] R. Findeisen, M. A. Grover, C. Wagner, M. Maiworm, R. Temirov, F. S. Tautz, M. V. Salapaka, S. Salapaka, R. D. Braatz, and S. O. R. Moheimani. Control on a molecular scale: A perspective. In Amer. Control Conf. (ACC), pages 3069--3082, 2016.
[189] T. Mühlpfordt, J. Paulson, R. Braatz, and R. Findeisen. Output Feedback Model Predictive Control with Probabilistic Uncertainties for Linear Systems. In Proc.Amer.Contr. Conf., ACC16, pages 2035--2040, 2016.
[188] B. Jabarivelisdeh and S. Waldherr. Improving bioprocess productivity using constraint-based models in a dynamic optimization scheme. IFAC-PapersOnLine, 49(26):245--251, 2016.
[187] C. Kallies, M. Schliemann-Bullinger, R. Findeisen, S. Lucia, and E. Bullinger. Monotonicity of Kinetic Proofreading. In IFAC-PapersOnLine - Proc. 6th Foundations Syst. Biology in Eng. FOSBE 2016, 9-12 October, Magdeburg, Germany, volume 49, pages 306--311, 2016.
[186] M. Kishida, M. Kögel, and R. Findeisen. Verifying robust forward admissibility for nonlinear systems using (skewed) structured singular values. In Proc. IEEE Conf. Decision and Control, pages 4065--4071, 2016.
[185] M. Kögel and R. Findeisen. Sampled-data, output feedback predictive control of uncertain, nonlinear systems. In Proc. IFAC Symp. Nonlinear Control Syst., pages 47 -- 52, 2016.
[184] M. Kögel and R. Findeisen. Output feedback MPC with send-on-delta measurements for uncertain systems. In Proc. IFAC Workshop Distributed Estimation and Control in Networked Syst., pages 145--150, 2016.
[183] S. Lucia, M. Schliemann-Bullinger, R. Findeisen, and E. Bullinger. A set-based optimal control approach for pharmacokinetic / pharmacodynamic drug dosage design. In Proc. DYCOPS-CAB 2016, pages 797--802, 2016.
[182] H. Rewald and O. Stursberg. Cooperation of autonomous vehicles using a hierarchy of auction-based and model-predictive control. In 2016 IEEE Intelligent Vehicles Symp. (IV), pages 1078--1084, 2016.
[181] K. Rinke, F. Jost, R. Findeisen, T. Fischer, R. Bartsch, E. Schalk, and S. Sager. Parameter estimation for leukocyte dynamics after chemotherapy. In IFAC-PapersOnLine - Proc. 6th Foundations Syst. Biology in Eng. FOSBE 2016, 9-12 October, Magdeburg, Germany, volume 49, pages 44--49, 2016.
[180] N. Rudolph, S. Streif, and R. Findeisen. Set-based experiment design for model discrimination using bilevel optimization. In Proc. Foundations Syst. Biology in Eng. (FOSBE), pages 295--299, 2016.
[179] S. Yu, T. Qu, R. Findeisen, and H. Chen. Model predictive control for uncertain nonlinear systems subject to chance constraints. In Decisi and Ctrol (CDC), 2016 IEEE 55th Cf., pages 2751--2756, 2016.
[178] P. Zometa, H. Heinemann, S. Lucia, M. Kögel, and R. Findeisen. Efficient stochastic model predictive control for embedded systems based on second-order cone programs. In Proc. Eur. Control Conf., pages 166--171, 2016.
[177] G. Takács, P. Zometa, R. Findeisen, and B. Rohal-Ilkiv. Efficiency and performance of embedded model predictive control for active vibration attenuation. In Proc. Eur. Control Conf. (ECC), pages 1334--1340, 2016.
[176] P. Andonov, A. Savchenko, P. Rumschinski, S. Streif, and R. Findeisen. Controller Verification and Parametrization Subject to Quantitative and Qualitative Requirements. In Int. Symp. Advanced Ctrol Chemical Processes (ADCHEM), pages 1174--1179, 2015.
[175] M. Kögel and R. Findeisen. Robust output feedback model predictive control using reduced order models. In Proc. Int. Symp. Advanced Ctrol Chemical Processes (ADCHEM), pages 1008--1014, 2015.
[174] M. Kopf, H.-G. Giesseler, P. Varutti, T. Faulwasser, and R. Findeisen. On the effect of enforcing stability in model predictive control for gust load alleviation. In Proc. Amer. Contr. Conf., ACC'14, pages 2329--2334, 2015.
[173] M. Kishida and R. Findeisen. μ-based Approaches to Determining Guaranteed Consistent and Inconsistent Parameter Sets. In Proc.54th IEEE Conf. Decision Contr., CDC15, pages 6603--6608, 2015.
[172] M. Kögel and R. Findeisen. Robust output feedback model predictive control using reduced order models. In Proc. IFAC Symp. Advanced Control Chemical Processes, pages 1009--1015, 2015.
[171] M. Kögel and R. Findeisen. Discrete-time robust model predictive control for continuous-time nonlinear systems. In Proc. Amer. Control Conf., pages 924--930, 2015.
[170] M. Kögel and R. Findeisen. Self-triggered, prediction-based control of Lipschitz nonlinear systems. In Proc. Eur. Control Conf., pages 2155--2160, 2015.
[169] M. Kögel and R. Findeisen. Robust output feedback predictive control with self-triggered measurements. In Proc. IEEE Conf. Decision and Control, pages 5487--5493, 2015.
[168] S. Lucia, P. Zometa, M. Kögel, and R. Findeisen. Efficient stochastic model predictive control based on polynomial chaos expansions for embedded applications. In Proc. IEEE Cf. Decisi and Ctrol (CDC), pages 3006--3012, 2015.
[167] S. Lucia, M. Kögel, and R. Findeisen. Contract-based predictive control of distributed systems with plug and play capabilities. In Proc. IFAC Cf. Nlinear Model Predictive Ctrol (NMPC), pages 205--211, 2015.
[166] S. Lucia, M. Kögel, P. Zometa, D. Quevedo, and R. Findeisen. Predictive control in the era of networked control and communication - a perspective. In Proc. IFAC Cf. Nlinear Model Predictive Ctrol (NMPC), pages 322-- 331, 2015.
[165] S. Lucia, P. Rumschinski, A. Krener, and R. Findeisen. Improved design of nonlinear model predictive controllers. In Proc. 5th IFAC Cf. Nlinear Model Predictive Ctrol (NMPC), pages 254--259, 2015.
[164] M. Maiworm, T. Bäthge, and R. Findeisen. Scenario-based model predictive control: Recursive feasibility and stability. In Proc. 9th Int. Symp. Advanced Ctrol Chemical Processes (ADCHEM), pages 50--56, 2015.
[163] J. Matschek, A. Himmel, F. von Haeseler, E. Bullinger, M. Skalej, and R. Findeisen. Mathematical modelling and sensitivity analysis of multipolar radiofrequency ablation in the spine. In Proc. 9th IFAC Symp. Biological and Medical Syst. (BMS), pages 243--248, 2015.
[162] B. Morabito, M. Kögel, E. Bullinger, G. Pannocchia, and R. Findeisen. Simple and efficient moving horizon estimation based on the fast gradient method. IFAC-PapersOnLine, 48(23):428 -- 433, 2015. 5th IFAC Conference on Nonlinear Model Predictive Control NMPC 2015.
[161] A. Patrascu, I. Necoara, and R. Findeisen. Rate of convergence analysis of a dual fast gradient method for general convex optimization. In Proc. 54th IEEE Cf. Decisi and Ctrol, pages 3311--3316, 2015.
[160] N. Rudolph, T. Meyer, K. Franzen, C. Garbers, S. Streif, F. Schaper, A. Dittrich, and R. Findeisen. A two-level approach for fusing early signaling events and long term biological responses. In Int. Symp. Advanced Ctrol Chemical Processes (AdChem), pages 1229--1234, 2015.
[159] M. Schliemann-Bullinger, M. Readman, D. Kalamatianos, R. Findeisen, and E. Bullinger. Unraveling apoptosis signalling using linear control methods: Linking the loop gain to reverting the decision to undergo apoptosis. In Proc. Int. Symp. Advanced Ctrol Chemical Processes (ADCHEM), volume 48, pages 954--959, 2015.
[158] M. Rausch, R. Klein, S. Streif, C. Pankiewitz, and R. Findeisen. Set-based state of charge estimation for Lithium-ion batteries. In Proc.Amer.Contr. Conf., ACC14, pages 1566--1571, 2014.
[157] S. Streif, D. Henrion, and R. Findeisen. Probabilistic and set-based model invalidation and estimation using LMIs. In Proc. 19th IFAC World Congr., pages 4110--4115, 2014.
[156] S. Streif, M. Kögel, T. Bäthge, and R. Findeisen. Robust nonlinear model predictive control with constraint satisfaction: A relaxation-based approach. In Proc. 19th IFAC World Congr., pages 11073--11079, 2014.
[155] S. Streif, F. Petzke, A. Mesbah, R. Findeisen, and R. Braatz. Optimal experimental design for probabilistic model discrimination using polynomial chaos. In Proc. 19th IFAC World Congr., pages 4103--4109, 2014.
[154] M. Kögel and R. Findeisen. On MPC based trajectory tracking. In Proc. Eur. Control Conf., pages 121--127, 2014.
[153] M. Kögel and R. Findeisen. On self-triggered reduced-attention control for constrained systems. In Proc. IEEE Conf. Decision and Control, pages 2795--2801, 2014.
[152] M. Kögel and R. Findeisen. Stabilization of inexact MPC schemes. In Proc. IEEE Conf. Decision and Control, pages 5922--5928, 2014.
[151] J. Matschek, F. von Haeseler, and R. Findeisen. Mathematical modeling of mono- and multipolar radio frequency ablation in the spine. In Proc. 1st Cf. Image Guided Interventis (IGIC), pages 69--70, 2014.
[150] A. Mesbah, S. Streif, R. Findeisen, and R. Braatz. Stochastic Nonlinear Model Predictive Control with Probabilistic Constraints. In Proc.Amer.Contr. Conf., ACC14, pages 2413--2419, 2014.
[149] A. Mesbah, S. Streif, R. Findeisen, and R. Braatz. Active Fault Diagnosis for Nonlinear Systems with Probabilistic Uncertainties. In Proc. 19th IFAC World Congr., pages 7079--7084, 2014.
[148] J. A. Paulson, D. M. Raimondo, R. Braatz, R. Findeisen, and S. Streif. Guaranteed Active Fault Diagnosis for Uncertain Nonlinear Systems. In Proc. Eur. Control Conf. (ECC), pages 926--931, 2014.
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[32] R. Findeisen and F. Allgöwer. Stabilization Using Sampled-data Open-Loop Feedback - a Nonlinear Model Predictive Control Perspective. In Proc. 6th. IFAC Symp. Nlinear Ctrol Systems, NOLCOS04, pages 735--740, 2004.
[31] R. Findeisen and F. Allgöwer. Min-max output feedback predictive control with guaranteed stability. In Proc. 16th Int. Symp. Math. Theory Networks and Systems, MTNS04, pages ISBN 90--5682--517--8, 2004.
[30] R. Lepore, R. Findeisen, A. Vande Wouwer, F. Allgöwer, and M. Remy. On open- and closed-loop control of an Mma polymerization reactor. In Proc. 23rd Benelux Meeting Syst. and Ctrol, 2004.
[29] R. Lepore, R. Findeisen, Z. Nagy, F. Allgöwer, and A. Vande Wouwer. Optimal Open- and Closed-Loop Control for Disturbance Rejection in Batch Process Control: a Mma Polymerization Example. In Proc. Symp. Knowledge Driven Batch Processes, BatchPro, 2004.
[28] Z. Nagy, R. Findeisen, and F. Allgöwer. Hierarchical nonlinear model predictive control of an industrial batch reactor. In Proc. Symp. Knowledge Driven Batch Processes, BatchPro, 2004.
[27] T. Raff, R. Findeisen, C. Ebenbauer, and F. Allgöwer. Model Predictive Control For Discrete Time Polynomial Control Systems: A convex Approach. In Proc.2nd IFAC Symp. System, Structure and Ctrol, SSSC04, pages 158--163, 2004.
[26] A. Yonchev, R. Findeisen, C. Ebenbauer, and F. Allgöwer. Model Predictive Control of Linear Continuous Time Singular Systems Subject to Input Constraints. In Proc.43th IEEE Conf. Decision Contr., CDC04, pages 2047--2052, 2004.
[25] P. H. Menold, R. Findeisen, and F. Allgöwer. Finite time convergent observers for nonlinear systems. In Proc.42th IEEE Conf. Decision Contr., CDC03, pages 5673--5678, December 2003.
[24] M. Diehl, R. Findeisen, F. Allgöwer, J. Schlöder, and H. Bock. Stability of Nonlinear Model Predictive Control in the Presence of Errors due to Numerical Online Optimization. In Proc.42th IEEE Conf. Decision Contr., CDC03, pages 1419--1424, 2003.
[23] R. Findeisen, L. Imsland, F. Allgöwer, and B. Foss. Stability Conditions for Observer Based Output Feedback Stabilization with Nonlinear Model Predictive Control. In Proc.42th IEEE Conf. Decision Contr., CDC03, pages 1425--1430, 2003.
[22] R. Findeisen, L. Imsland, F. Allgöwer, and B. Foss. Output-feedback Nonlinear Model Predictive Control using High-Gain Observers in Original Coordinates. In 7th Eur. Control Conference, ECC2003, pages 2061--2066, 2003.
[21] R. Findeisen and F. Allgöwer. Theorie und Anwendung der nichtlinearen prädiktiven Regelung. In Proc. GMA-Gesellschaft für Meß- und Automatisierungstechnik Annu. Meeting, 2003.
[20] L. Imsland, R. Findeisen, F. Allgöwer, and B. Foss. Output Feedback Stabilization with Nonlinear Predictive Control - Asymptotic properties. In Proc. Amer.Contr.Conf., ACC03, pages 4908--4913, 2003.
[19] P. H. Menold, R. Findeisen, and F. Allgöwer. Finite time convergent observers for linear time-varying systems. In Proc. 11th Mediterranean Cf. Ctrol and Automati, MED03, 2003.
[18] R. Findeisen, L. Imsland, F. Allgöwer, and B. Foss. Output feedback nonlinear predictive control - A separation principle approach. In Proc. 15th IFAC World Congr., 2002.
[17] R. Findeisen, M. Diehl, and T. Bürner. Efficient Output Feedback Nonlinear Model Predictive Control. In Proc.Amer.Contr. Conf., ACC02, pages 4752--4757, 2002.
[16] R. Findeisen, M. Diehl, I. Disli, S. Schwarzkopf, F. Allgöwer, H. Bock, J. Schlöder, and Gilles. Computation and Performance Assessment of Nonlinear Model Predictive Control. In Proc. 41th IEEE Conf. Decision Contr., CDC 02, pages 4613--4618, 2002.
[15] R. Findeisen and F. Allgöwer. Nonlinear Model Predictive Control: From Theory to Application. In Proc. Int. Symp. Design, Operati and Ctrol Chemical Plants, PSE Asia02, Taipei, Taiwan, 2002.
[14] F. Allgöwer, J. Rossitter, M. Cannon, B. Kouvaritakis, H. Bock, M. Diehl, R. Findeisen, and J. Schlöder. Tutorial Workshop on Computational efficiency in linear and non-linear predictive control. In 15th IFAC World Congr., 2002.
[13] Z. Nagy, S. Agachi, F. Allgöwer, R. Findeisen, M. Diehl, H. Bock, and J. Schlöder. The tradeoff between modelling complexity and real-time feasibility in nonlinear model predictive control. In Proc. 6th World Multicference Systemics, Cybern. and Informatics, SCI 2002, pages 329--334, 2002.
[12] E. Bullinger, R. Findeisen, and F. Allgöwer. Adaptive λ-tracking of nonlinear systems with higher relative degree using reduced-order high gain control. In Proc. Symp. Nlinear Ctrol Systems, NOLCOS'01, pages 92--97, 2001.
[11] R. Findeisen, Z. Nagy, M. Diehl, F. Allgöwer, H. Bock, and J. Schlöder. Computational feasibility and performance of nonlinear model predictive control. In Proc.6st Eur. Control Conference, ECC01, pages 957--961, 2001.
[10] R. Findeisen and F. Allgöwer. An introduction to nonlinear model predictive control. In C. Scherer and J. Schumacher, editors, Summerschool "The Impact Optimizati in Ctrol", Dutch Inst. Syst. and Ctrol, DISC, pages 3.1--3.45, 2001.
[9] F. Allgöwer and R. Findeisen. Nonlinear Model Predictive Control of Chemical Processes. In B. Maschke and A. Van der Schaft, editors, Workshop Geometrical Modeling and Ctrol Physical Syst., pages 3.1--3.75, 2001.
[8] L. Imsland, R. Findeisen, E. Bullinger, F. Allgöwer, and B. Foss. On Output feedback Nonlinear Model Predictive Control using high gain observers for a class of systems. In 6th IFAC Symp. Dynamics and Ctrol Process Systems, DYCOPS-6, pages 91--96, 2001.
[7] Z. Nagy, S. Agachi, F. Allgöwer, and R. Findeisen. Nonlinear model predictive control of a high purity distillation column. In 14-th Int. Congr. Chemical and Process Eng. CHISA 2000, 2001.
[6] Z. Nagy, S. Agachi, F. Allgöwer, R. Findeisen, M. Diehl, H. Bock, and J. Schlöder. Using Genetic Algorithm in Robust Nonlinear Model Predictive Control. In Eur. Symp. Comput. Aided Process Engineering-11, ESCAPE-11, pages 711--716, 2001.
[5] F. Allgöwer, R. Findeisen, Z. Nagy, M. Diehl, H. Bock, and J. Schlöder. Efficient Nonlinear Model Predictive Control for Large Scale Constrained Processes. In Proc. Sixth Int. Cf. Methods and Models in Automati and Robotics, pages 43--54, 2000.
[4] E. Bullinger, R. Findeisen, F. Kraus, and F. Allgöwer. Some further results on adaptive λ-tracking for linear systems with high relative degree. In Proc. Amer. Contr. Conf., ACC'00, pages 3655--3660, 2000.
[3] R. Findeisen, H. Chen, and F. Allgöwer. Nonlinear Predictive Control for Setpoint Families. In Proc.Amer.Contr. Conf., ACC00, pages 260--264, 2000.
[2] R. Findeisen, F. Allgöwer, M. Diehl, H. Bock, J. Schlöder, and Z. Nagy. Efficient Nonlinear Model Predictive Control. In 6th Int. Cf. Chemical Process Ctrol, CPC VI, pages 454--460, 2000.
[1] Z. Nagy, R. Findeisen, M. Diehl, F. Allgöwer, H. Bock, S. Agachi, J. Schlöder, and D. Leineweber. Real-time Feasibility of Nonlinear Predictive Control for Large Scale Processes - a Case Study. In Proc.Amer.Contr. Conf., ACC00, pages 4249--4253, 2000.

Submitted Proceedings (accepted)

[7] P. Andonov, A. Savchenko, P. Rumschinski, T. Trenner, J. Neidig, and R. Findeisen. Monitoring and Verification of Event-driven Discrete Manufacturing Systems with Guarantees. In Cf. Ctrol Technol. and Applicatis (CCTA), 2020. accepted.
[6] J. Bethge, B. Morabito, H. Rewald, A. Ahsan, and R. Findeisen. Model predictive control of mixed traffic at intersections using learning and classification of human driving behavior. In Proc. IFAC World Congr., 2020. Accepted.
[5] J. Bethge, S. Yu, and R. Findeisen. Model predictive control with guarantees for discrete linear stochastic systems subject to additive disturbances with chance constraints. In Proc. Amer. Control Conf. (ACC), 2020. Accepted.
[4] J. Matschek, A. Himmel, K. Sundmacher, and R. Findeisen. Constrained Gaussian process learning for model predictive control. In Proc. 20th IFAC World Congr. Berlin, 2020. to appear.
[3] J. Matschek, R. Jordanowa, and R. Findeisen. Direct Robotic Force Control with Learning Supported Model Predictive Control. 2020. accepted.
[2] B. Jabarivelisdeh, R. Findeisen, and S. Waldherr. Model predictive control of a fed-batch bioreactor based on dynamic metabolic-genetic network models. In Int. Cf. Foundatis Syst. Biology in Eng. (FOSBE), August 2018.
[1] B. Morabito, M. Kögel, S. Blasi, V. Klemme, C. Hansen, O. Höhn, and R. Fideisen. Multi-stage event-triggered model predictive control for automated trajectory drilling. Submitted.

Submitted Proceedings (to be peer reviewed)

[3] H. H. Nguyen, J. Matschek, T. Zieger, A. Savchenko, and R. Findeisen. Towards nominal stability certification of deep learning-based controllers. 2020. accepted.
[2] H. H. Nguyen, B. Morabito, and R. Findeisen. Repetitive set-based learning robust predictive control for lur'e systems. 2019. submitted.
[1] T. Zieger, A. Savchenko, T. Oehlschlägel, and R. Findeisen. Towards safe neural network supported model predictive control. 2019. submitted.

Technical Report

[2] S. Raković, B. Kern, and R. Findeisen. Practical robust positive invariance for large-scale discrete time systems. Technical Report IFAT-SYS 1/2011, 2011.
[1] S. Raković, B. Kern, and R. Findeisen. Practical set invariance for decentralized discrete time systems. Technical Report IFAT-SYS 1/2010, 2010.

Miscellaneous(peer reviewed)

[2] S. Borchers, P. Rumschinski, S. Bosio, and R. Findeisen. Model invalidation and discrimination of biochemical reaction networks based on graph approximation, August 2008.
[1] M. Schliemann, S. Borchers, P. Scheurich, and E. Bullinger. TNF-? induced apoptotc and anti-apoptotic signalling: A systems biology approach combining dynamic and quantitative experiments with mathematic modelling, August 2008.

Patent applications

[1] F. Quattrone, C. Hansen, O. Hoehn, J. Koeneke, B. Morabito, and R. Findeisen. Model-based parameter estimation for directional drilling in wellbore operations, 2019. Application number: US15935659.