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[107]
|
S. Espinel-Rios, K. Bettenbrock, S. Klamt, and R. Findeisen.
Maximizing batch fermentation efficiency by constrained model-based
optimization and predictive control of adenosine triphosphate turnover.
AIChE J., 68(4):e17555, 2022.
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[106]
|
J. Matschek, J. Bethge, and R. Findeisen.
Safe machine-learning-suppored model predictive force and motion
control in robotics.
2022.
accepted.
|
|
[105]
|
J. Bethge, R. Findeisen, D. D. Le, M. Merkert, H. Rewald, S. Sager,
A. Savchenko, and S. Sorgatz.
Mathematical optimization and machine learning for efficient urban
traffic.
German Success Stories in Industrial Mathematics, 35:113--120,
2021.
|
|
[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, pages
1--28, 2021.
|
|
[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.
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|
[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 cardiomyopathyâthe 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.
|
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[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.
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[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.
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[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.
|
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[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.
|
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[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.
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[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.
|
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[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.
|
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[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.
|
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[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.
|
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[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.
|
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[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.
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[49]
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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.
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[48]
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Z. Artstein and S. Raković.
Set Invariance Under Output Feedback: A set-Dynamics
Approach.
Int. J. of Systems Science, 42(4):539--555, 2010.
|
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[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.
|
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[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.
|
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[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.
|
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[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.
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[42]
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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.
|
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[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.
|
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[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.
|
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[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.
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[37]
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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.
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[36]
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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.
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[35]
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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.
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[34]
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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.
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[33]
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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.
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[32]
|
E. Bullinger, D. Fey, M. Farina, and R. Findeisen.
Identifikation biochemischer Reaktionsnetzwerke: Ein
beobachterbasierter Ansatz.
AT-Automatisierungstechnik, 56(5):269--279, 2008.
|
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[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.
|
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[29]
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C. Orwat, A. Graefe, and T. Faulwasser.
Towards pervasive computing in health care - a literature review.
BMC Medical Informatics and Decision Making, 8, 2008.
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[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.
|
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[27]
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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.
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[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.
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[25]
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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.
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[24]
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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.
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[23]
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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.
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[22]
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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.
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[21]
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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.
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[20]
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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.
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[19]
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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.
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[18]
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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.
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[17]
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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.
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[16]
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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.
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[15]
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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.
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[14]
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F. Allgöwer, R. Findeisen, and C. Ebenbauer.
Nonlinear Model Predictive Control.
Encyclopedia for Life Support Systems (EOLSS) article contribution
6.43.16.2, 2003.
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[13]
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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.
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[12]
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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.
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[11]
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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.
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[10]
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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.
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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.
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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.
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[7]
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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.
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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.
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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.
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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.
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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.
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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.
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H. Lindhorst, S. Lucia, R. Findeisen, and S. Waldherr.
Modelling metabolic networks including gene expression and
uncertainties.
arXiv preprint arXiv:1609.08961.
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[272]
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J. Bethge, M. Pfefferkorn, A. Rose, J. Peters, and R. Findeisen.
Model predictive control with gaussian-process-supported dynamical
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accepted.
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[271]
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T. Zieger, P. Holzmann, J. Bethge, T. Oehlschl"gel, and R. Findeisen.
Rigid-tube nonlinear model predictive control for path following.
In Proc. IFAC World Congr., 2023.
accepted.
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[270]
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C. Eckel, M. Maiworm, and R. Findeisen.
Optimal operation and control of towing kites using online and
offline Gaussian process learning supported model predictive control.
In Amer. Control Conf. (ACC), pages 2637--2643. IEEE, 2022.
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[269]
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S. Espinel-Rios, B. Morabito, J. Pohlodek, K. Bettenbrock, S. Klamt, and
R. Findeisen.
Optimal control and dynamic modulation of the ATPase gene expression
for enforced ATP wasting in batch fermentations.
IFAC-PapersOnLine, 55(7):174--180, 2022.
13th IFAC Symposium on Dynamics and Control of Process Systems,
including Biosystems DYCOPS 2022.
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[268]
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P. Holzmann, J. Matschek, M. Pfefferkorn, and R. Findeisen.
Learning secure corridors for model predictive path following
control of autonomous systems in cluttered environments.
In Proc. Eur. Control Conf. (ECC), pages 1772--1777, 2022.
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[267]
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B. Morabito, H. H. Nguyen, J. Matschek, and R. Findeisen.
Safe Exploration Learning Supported Model Predictive Control of
Repetitive Processes.
In 2022 Amer. Control Conf. (ACC), pages 2631--2636. IEEE,
2022.
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[266]
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B. Morabito, J. Pohlodek, L. Kranert, S. Espinel-Rios, and R. Findeisen.
Efficient and Simple Gaussian Process Supported Stochastic Model
Predictive Control for Bioreactors using HILO-MPC.
IFAC-PapersOnLine, 55(7):922--927, 2022.
13th IFAC Symposium on Dynamics and Control of Process Systems,
including Biosystems DYCOPS 2022.
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[265]
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H. H. Nguyen, M. Pfefferkorn, and R. Findeisen.
High-probability stable Gaussian process-supported model predictive
control for Lur’e systems.
In Proc. Eur. Control Conf. (ECC), 2022.
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M. Pfefferkorn, M. Maiworm, and R. Findeisen.
Exact multiple-step predictions in Gaussian process-based model
predictive control: Observations, possibilities, and challenges.
In Amer. Control Conf. (ACC), pages 2829--2836. IEEE, 2022.
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M. Pfefferkorn, P. Holzmann, J. Matschek, and R. Findeisen.
Safe corridor learning for model predictive path following control.
In Proc. 25th Int. Symp. Mamatics Networks and Syst. (MTNS),
2022.
(accepted).
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[262]
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S. Duvigneau, R. Dürr, L. Kranert, A. Wilisch-Neumann, L. Carius,
R. Findeisen, and A. Kienle.
Hybrid cybernetic modeling of the microbial production of
polyhydroxyalkanoates using two carbon sources.
50:1969--1974, 2021.
31st European Symposium on Computer Aided Process Engineering.
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[261]
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M. Kögel and R. Findeisen.
Reducing conservatism in stochastic model predictive blending
multiple control gains.
In Proc. Amer. Control Conf., pages 2094--2101, 2021.
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[260]
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M. Kögel, D. Quevedo, and R. Findeisen.
Robust MPC for networks with varying communication capabilities.
In Proc. IFAC Nonlinear Model Predictive Control Conf., pages
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M. Kögel and R. Findeisen.
Robust output feedback MPC with reduced conservatism for linear
uncertain systems using time varying tubes.
In Proc. IEEE Conf. Decision and Control, pages 2571--2577,
2021.
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[258]
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J. Matschek, A. Himmel, and R. Findeisen.
Constrained learning for model predictive control in asymptotically
constant reference tracking tasks.
In Proc. 19th IFAC Symp. for System Identification, pages
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[257]
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J. Matschek, J. Bethge, M. Soliman, B. Elsayed, and R. Findeisen.
Constrained reference learning for continuous-time model predictive
tracking control of autonomous systems.
volume 54, pages 329--334, 2021.
Proceedings of the 7th IFAC Conference on Nonlinear Model Predictive
Control.
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[256]
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B. Morabito, J. Pohlodek, J. Matschek, A. Savchenko, L. Carius, and
R. Findeisen.
Towards risk-aware machine learning supported model predictive
control and open-loop optimization for repetitive processes.
IFAC-PapersOnLine, 54(6):321--328, 2021.
7th IFAC Conference on Nonlinear Model Predictive Control NMPC 2021.
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[255]
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H. H. Nguyen, T. Zieger, S. C. Wells, A. Nikolakopoulou, R. D. Braatz, and
R. Findeisen.
Stability certificates for neural network learning-based controllers
using robust control theory.
In 2021 Amer. Control Conf. (ACC), pages 3564--3569, 2021.
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H. H. Nguyen, T. Zieger, R. D. Braatz, and R. Findeisen.
Robust control theory based stability certificates for neural network
approximated nonlinear model predictive control.
In Proc. 7th Nonlinear Model Predictive Control Conf. (NMPC),
pages 347--352, 2021.
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T. Zieger, A. Savchenko, T. Oehlschlägel, and R. Findeisen.
One-step safe neural network supported control.
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[252]
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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), pages 1029--1035,
2020.
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[251]
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J. Bethge, B. Morabito, H. Rewald, A. Ahsan, S. Sorgatz, and R. Findeisen.
Model predictive control of mixed traffic at intersections using
learning and classification of human driving behavior.
In Proc. IFAC World Congr., pages 14557--14563, 2020.
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[250]
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J. Bethge, S. Yu, and R. Findeisen.
Model predictive control with guarantees for discrete linear
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In 2020 Amer. Control Conf. (ACC), pages 1943--1948, 2020.
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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.
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[248]
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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.
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[247]
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C. Kallies, M. Ibrahim, and R. Findeisen.
Approximated Explicit Infinite Horizon Constraint Optimal
Control for Systems with Parametric Uncertainties.
In Proc. 21st IFAC World Congress. Berlin, Germany, 2020.
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[246]
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C. Kallies, M. Ibrahim, and R. Findeisen.
Fallback Approximated Constrained Optimal Output Feedback
Control Under Variable Parameters.
In Portuguese Cf. Automat. Ctrol, pages 404--414. Springer,
2020.
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[245]
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M. Kögel and R. Findeisen.
Robust MPC with reduced conservatism by blending multiples tubes.
In Proc. Amer. Control Conf., pages 1949--1954, 2020.
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[244]
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M. Kögel and R. Findeisen.
Fusing multiple time varying tubes for robust MPC.
In Proc. IFAC World Congr., pages 7137--7144, 2020.
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[243]
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J. Matschek, A. Himmel, K. Sundmacher, and R. Findeisen.
Constrained Gaussian process learning for model predictive control.
In Proc. 20th IFAC World Congr. Berlin, pages 971--976, 2020.
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[242]
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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.
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[241]
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J. Matschek and R. Findeisen.
Learning supported Model Predictive Control for Tracking of Periodic
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In Proc. Mach. Learning Res., volume 120, pages 511--520, 2020.
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J. Matschek, R. Jordanowa, and R. Findeisen.
Direct Robotic Force Control with Learning Supported Model
Predictive Control.
In Proc. Cf. Ctrol Technol. and Applicatis (CCTA), pages 8--13,
2020.
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[239]
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B. Morabito, M. Kögel, S. Blasi, V. Klemme, C. Hansen, O. Höhn, and
R. Findeisen.
Multi-stage event-triggered model predictive control for automated
trajectory drilling.
volume 53, pages 9478--9483, 2020.
21th IFAC World Congress.
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[238]
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H. H. Nguyen, J. Matschek, T. Zieger, A. Savchenko, N. Noroozi, and
R. Findeisen.
Towards nominal stability certification of deep learning-based
controllers.
In 2020 Amer. Control Conf. (ACC), pages 3886--3891, 2020.
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H. H. Nguyen, B. Morabito, and R. Findeisen.
Repetitive set-based learning robust predictive control for lur'e
systems.
In Proc. IFAC World Congr., pages 7117--7122, 2020.
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[236]
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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), pages 5525--5531. IEEE,
2020.
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[235]
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J. Pohlodek, A. Rose, B. Morabito, L. Carius, and R. Findeisen.
Data-driven Metabolic Network Reduction for Multiple Modes
Considering Uncertain Measurements.
IFAC-PapersOnLine, 53(2):16866--16871, 2020.
21st IFAC World Congress.
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[234]
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T. Zieger, A. Savchenko, T. Oehlschlägel, and R. Findeisen.
Towards safe neural network supported model predictive control.
2020.
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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.
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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.
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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.
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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),
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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.
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[228]
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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.
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[227]
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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.
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[226]
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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.
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[225]
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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.
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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.
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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.
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[222]
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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.
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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.
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[220]
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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.
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[219]
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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.
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[218]
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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.
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[217]
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M. Kögel and R. Findeisen.
Improved robust decentralized MPC.
In Proc. Eur. Control Conf., pages 312--318, 2018.
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[216]
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M. Kögel and R. Findeisen.
Towards Optimal Tuning of Robust Output Feedback MPC.
In Proc. IFAC Nonlinear Model Predictive Control Conf., pages
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[215]
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M. Kögel and R. Findeisen.
Low latency output feedback predictive control based on optimality
conditions.
PAMM, 18(1):e201800429, 2018.
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[214]
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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. IEEE, 2018.
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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
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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.
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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.
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[210]
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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.
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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
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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.
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[207]
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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.
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[206]
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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
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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.
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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.
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[203]
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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.
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[202]
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M. Kögel and R. Findeisen.
Robust output feedback MPC for uncertain linear systems with reduced
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In Proc. IFAC World Congr., pages 10685--10690, 2017.
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M. Kögel and R. Findeisen.
Low latency output feedback model predictive control for constrained
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In Proc. IEEE Conf. Decision and Control, pages 1925--1932,
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[200]
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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,
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[199]
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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.
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[198]
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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.
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[197]
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S. Lucia, M. Torchio, D. M. Raimondo, R. Klein, R. D. Braatz, and R. Findeisen.
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Unraveling apoptosis signalling using linear control methods: Linking
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Einsatz cyber-physischer Systeme im Echtzeitkontext:
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Event-based NMPC for networked control systems over UDP-like
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M. Kögel and R. Findeisen.
A fast gradient method for embedded linear predictive control.
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M. Kögel and R. Findeisen.
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S. Raković, B. Kern, and R. Findeisen.
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systems.
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A. Savchenko, P. Rumschinski, and R. Findeisen.
Fault diagnosis for polynomial hybrid systems.
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T. Faulwasser and R. Findeisen.
Constrained output path-following for nonlinear systems using
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In Proc. 8th. IFAC Symp. Nlinear Ctrol Systems, NOLCOS'10,
pages 753--758, 2010.
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R. Klein, N. Chaturvedi, J. Christensen, R. Ahmed, J. Findeisen, and A. Kojic.
State Estimation of a Reduced Electrochemical Model of a
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Modeling, estimation and control challenges for Lithium-Ion
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S. Maldonado, A. Savchenko, and R. Findeisen.
Therapy discrimination via global sensitivity analysis of
force-induced bone growth and adaptation.
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Force-induced bone growth and adaptation: A system theoretical
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M. Cannon, C. Q, B. Kouvaritakis, and S. Raković.
Stochastic tube Mpc with state estimation.
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B. Kouvaritakis, M. Cannon, S. Raković, and C. Q.
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S. Raković, B. Kouvaritakis, R. Findeisen, and M. Cannon.
Simple Homothetic Tube Model Predictive Control.
In Proc. 19th Int. Symp. Math. Theory Networks and Systems,
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P. Rumschinski, J. Richter, A. Savchenko, S. Borchers, J. Lunze, and
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Complete fault diagnosis of uncertain polynomial systems.
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pages 127--132, 2010.
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Advances in global parameter estimation approaches for biochemical
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P. Rumschinski, D. S. Laila, S. Borchers, and R. Findeisen.
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P. Varutti, B. Kern, and R. Findeisen.
Event-based control for networked control systems: An active
compensation approach.
In Proc. Entwurf komplexer Automatisierungssysteme, EKA'10,
pages 29--38, 2010.
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Optimal control over unreliable networks with uncertain loss rates.
In Proc. Amer. Control Conf., pages 3672--3677, 2010.
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S. Raković, B. Kern, and R. Findeisen.
Practical set invariance for decentralized discrete time systems.
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2010.
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P. Varutti, T. Faulwasser, B. Kern, M. Kögel, and R. Findeisen.
Event-based reduced-attention predictive control for nonlinear
uncertain systems.
In Proc. IEEE Multi-Conf. Syst. and Control, pages 1085--1090,
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S. Borchers, P. Rumschinski, S. Bosio, and R. Weismantel, R.and Findeisen.
Model invalidation and system identification of biochemical reaction
networks.
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S. Borchers, P. Rumschinski, S. Bosio, R. Weismantel, and R. Findeisen.
Model discrimination and parameter estimation via infeasibility
certificates for dynamical biochemical reaction networks.
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S. Borchers, P. Rumschinski, S. Bosio, R. Weismantel, and R. Findeisen.
A set-based framework for coherent model invalidation and parameter
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In 48th IEEE Cf. Decisi and Ctrol, pages 6786 -- 6792, 2009.
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T. Faulwasser, B. Kern, and R. Findeisen.
Model predictive path-following for constrained nonlinear systems.
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2009.
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J. Hasenauer, P. Rumschinski, S. Waldherr, S. Borchers, F. Allgöwer, and
R. Findeisen.
Guaranteed steady-state bounds for uncertain chemical processes.
In Proc. Int. Symp. Advanced Ctrol Chemical Processes ADCHEM09,
2009.
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S. Maldonado, F. Allgöwer, and R. Findeisen.
Global sensitivity analysis of force induced bone growth and
adaptation using semidefinite programming.
In Proc. Foundations Syst. Biology in Engineerings FOSBE'09,
pages 141 -- 144, 2009.
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M. Kearney, S. Raković, and P. McAree.
Control Correction Synthesis: A set-Theoretic Approach.
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S. Raković, M. Kearney, and P. McAree.
Robust Control Correction Synthesis: A set-Theoretic
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P. Varutti and R. Findeisen.
Predictive control of nonlinear chemical processes under asynchronous
measurements and controls.
In Proceedings Int. Symp. Advanced Ctrol Chemical Processes
ADCHEM09, pages 156 -- 161, 2009.
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P. Varutti and R. Findeisen.
Compensating network delays and information loss by predictive
control methods.
In Proc. Eur. Control Conf. ECC09, pages 1722 -- 1727, 2009.
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P. Varutti and R. Findeisen.
On the synchronization problem for the stabilization of networked
control systems over nondeterministic networks.
In Proc. Amer. Contr. Conf. ACC09, pages 2216 -- 2221, 2009.
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P. Varutti, B. Kern, T. Faulwasser, and R. Findeisen.
Event-based model predictive control for networked control systems.
In Proc. 48th IEEE Cf. Decisi and Ctrol, pages 567--572, 2009.
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C. Böhm, S. Yu, R. Findeisen, and F. Allgöwer.
Predictive control for Lure systems subject to constraints using
Lmis.
In Proc. 10th Eur. Control Conf. ECC09, pages 3389 -- 3394,
2009.
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D. Fey, R. Findeisen, and E. Bullinger.
Parameter estimation in kinetic reaction models using nonlinear
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In Proc. 17th IFAC World Congr., pages 313--318, 2008.
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C. Böhm, T. Raff, R. Findeisen, and F. Allgöwer.
Calculating the terminal region of NmpC for Lure Systems
via Lmis.
In Proc. Amer.Contr.Conf.
08, pages 1127--1132, 2008.
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C. Böhm, R. Findeisen, and F. Allgöwer.
Avoidance of Poorly Observable Trajectories: A predictive
Control Perspective.
In Proc. 17th IFAC World Congr., volume 17, pages 1952--1957,
2008.
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N. Dmitruk, R. Findeisen, and F. Allgöwer.
Optimal Measurement Feedback Control of Finite-Time
Continous Linear Systems.
In Proc. 17th IFAC World Congr., pages 15339--15344, 2008.
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D. Geffen, R. Findeisen, M. Schliemann, F. Allgöwer, and M. Guay.
Observability based parameter identifiability for biochemical
reaction networks.
In Proc. Amer. Contr. Conf. ACC, pages 2130--2135, 2008.
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S. Maldonado, R. Findeisen, and F. Allgöwer.
Understanding the process of force induced bone growth and adaptation
by a mathematical model.
In Proc. Comp. Modeling in Be Mechanobiology, 8th Wolrd Cgr.
Computatial Mechanics (WCCM8), 5th Eur. Cgr. Computatial Methods in Appl.
Sciences and Eng. (ECCOMAS 2008), Italy, 2008.
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S. Waldherr, R. Findeisen, and F. Allgöwer.
Global Sensitivity Analysis of Biochemical Reaction
Networks via Semidefinite Programming.
In Proc. 17th IFAC World Congr., volume 17, pages 9701--9706,
2008.
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U.-U. Haus, D. Michaels, and A. Savchenko.
Extended formulations for MINLP problems by value decompositions.
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M. Farina, E. Bullinger, R. Findeisen, and S. Bittanti.
An observer based strategy for parameter identification in systems
biology.
In Proc. Foundations Syst. Biology in Eng. FOSBE'07, pages
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Z. Nagy, R. Klein, A. Kiss, and R. Findeisen.
Advanced control of a reactive distillation column.
Proceedings of the 17th European Symposium on Computer Process
Engineering ESCAPE17, 24:805--810, 2007.
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D. Geffen, R. Findeisen, M. Schliemann, F. Allgöwer, and M. Guay.
The question of parameter identifiability for biochemical reaction
networks considering the NF-κ signal transduction pathway.
In Proc. Foundations Syst. Biology in Eng. FOSBE, Stuttgart,
Germany, pages 509--514, 2007.
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S. Maldonado, R. Findeisen, and F. Allgöwer.
Phenomenological mathematical modeling and analysis of force-induced
bone growth and adaptation.
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pages 147--152, 2007.
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R. Findeisen, J. Sjoberg, and F. Allgöwer.
Model Predictive Control of Continuous Time Nonlinear
Differential Algebraic Systems.
In Proc. Symp. Nlinear Ctrol Systems, NOLCOS
07, pages 165--171, 2007.
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S. Streif, R. Findeisen, and E. Bullinger.
Sensitivity analysis of biochemical reaction networks by bilinear
approximation.
In Proc. Foundations Syst. Biology in Eng. (FOSBE) Conference,
Stuttgart, Germany, pages 521--526, 2007.
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I. Alvarado, R. Findeisen, P. Kühl, D. Limón, and F. Allgöwer.
Iteratively Improving Moving Horizon Observers for
Repetitive Processes.
In Proc. joint CTS-HYCON workshop, 2006.
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S. Borchers, S. Maldonado, R. Findeisen, and F. Allgöwer.
Modeling the bone remodeling cycle due to mechanical force.
In Proc. 2nd Eur. Modeling and Simulation Symp. (EMSS'06),
pages 385--394, 2006.
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S. Maldonado, S. Borchers, R. Findeisen, and F. Allgöwer.
Mathematical modeling and analysis of force induced bone growth.
In Proc. 28th Annu. Int. Conf. IEEE Eng. in Medicine and
Biology Soc. EMBS'06, pages 3154--3157, 2006.
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S. Streif, R. Findeisen, and E. Bullinger.
Relating cross Gramian and sensitivity analysis in systems biology.
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E. Bullinger, R. Findeisen, D. Kalamatianos, and P. Wellstead.
System and control theory allows to further understanding of
biological signal transduction.
In Proc. Workshop CNRS-NSF Biology and control theory:
Current challenges, 2006.
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M. Farina, R. Findeisen, E. Bullinger, S. Bittanti, F. Allgöwer, and
P. Wellstead.
Results towards identifiability properties of biochemical reaction
networks.
In Proc. 45th IEEE Conf. Decision Contr., CDC'06, pages
2104--2109, 2006.
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M. Herceg, T. Raff, R. Findeisen, and F. Allgöwer.
Nonlinear Model Predictive Control of a Turbocharged
Diesel Engine.
In Proc. IEEE Cf. Ct. Appl. CCA06, pages
2766--2771, 2006.
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R. Lepore, A. Vande Wouwer, M. Remy, R. Findeisen, Z. Nagy, and
F. Allgöwer.
Scheduled optimization of an Mma polymerization process.
In Proc. Int. Symp. Adv. Control Chemical Processes,
ADCHEM06, pages 939--944, 2006.
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S. Maldonado, S. Borchers, R. Findeisen, and F. Allgöwer.
Modeling bone adaptation and remodeling initiated by mechanical
stimuli.
In Proc. 2nd Eur. Modeling and Simulation Symp. EMSS'06,
pages 403--409, 2006.
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S. Raković, R. Findeisen, D. Mayne, and F. Allgöwer.
Constrained Linear Systems under Uncertainty Based on
Feed Forward and Positive Invariant Feedback Control.
In Proc.45th IEEE Conf. Decision Contr.,
CDC06, pages 618--6623, 2006.
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D. Mayne, S. Raković, R. Findeisen, and F. Allgöwer.
Robust Output Feedback Model Predictive Control for
Constrained Linear Systems under Uncertainty Based on Feed
Forward and Positive Invariant Feedback Control.
In Proc. IEEE Cf. Dec. and Ctr., CDC 06, pages
6618--6623, 2006.
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T. Raff, R. Findeisen, J. Kim, and F. Allgöwer.
Control of Nonlinear Time-Delay Systems with Guaranteed
Stability: A model Predictive Control Perspective.
In Proc. Symp. Nlinear Ctrol Systems, NOLCOS
07, pages 134--139, 2006.
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I. Alvarado, R. Findeisen, P. Kühl, F. Allgöwer, and D. Limón.
State Estimation for Repetitive Processes Using
Iteratively Improving Moving Horizon Observers.
In Proc.joint 44th IEEE Conf. Decision Contr.,
CDC05/9th Eur. Control Conference, ECC05,
pages 7756--7761, 2005.
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R. Findeisen and F. Allgöwer.
Robustness Properties and Output Feedback of Optimization
Based Sampled-data Open-loop feedback.
In Proc.joint 44th IEEE Conf. Decision Contr.,
CDC05/9th Eur. Control Conference, ECC05,
pages 54--59, 2005.
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C. Ebenbauer, R. Findeisen, and F. Allgöwer.
Nonlinear High-Gain Observer Design via Semidefinite
Programming.
In Proc.2nd IFAC Symp. System, Structure and
Ctrol, SSSC04, pages 751--756, 2004.
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R. Findeisen and F. Allgöwer.
Computational Delay in Nonlinear Model Predictive
Control.
In Proc. Int. Symp. Adv. Control Chemical Processes,
ADCHEM03, pages 427--432, 2004.
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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.
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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.
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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.
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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,
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Z. Nagy, R. Findeisen, and F. Allgöwer.
Hierarchical nonlinear model predictive control of an industrial
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2004.
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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.
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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.
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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.
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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.
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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.
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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.
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R. Findeisen and F. Allgöwer.
Theorie und Anwendung der nichtlinearen prädiktiven
Regelung.
In Proc. GMA-Gesellschaft für Meß- und
Automatisierungstechnik Annu. Meeting, 2003.
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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.
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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.
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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.
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R. Findeisen, M. Diehl, and T. Bürner.
Efficient Output Feedback Nonlinear Model Predictive
Control.
In Proc.Amer.Contr.
Conf., ACC02, pages 4752--4757, 2002.
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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,
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