Publications

Laha, R.; Becker, M.; Vorndamme, J.; Vrabel, J.; Figueredo, L. F. C;.Müller, M. A. & Haddadin, S. (2024): Predictive Multi-Agent-Based Planning and Landing Controller for Reactive Dual-Arm Manipulation,  IEEE Transactions on Robotics, Vol. 40, pp. 864-885
DOI: 10.1109/TRO.2023.3341689

Nonhoff, M.; Dall'Anese, E. & Müller, M. A. (2024): Online convex optimization for robust control of constrained dynamical systems,  submitted
arXiv: 2401.04487

Schiller, J. D. & Müller, M. A. (2024): Robust stability of moving horizon estimation for continuous-time systems,  at - Automatisierungstechnik, Vol. 72, No. 2, pp. 120-133
DOI: 10.1515/auto-2023-0087
arXiv: 2305.06614

Schiller, J. D. & Müller, M. A. (2024): Moving horizon estimation for nonlinear systems with time-varying parameters,  IFAC-PapersOnLine, Vol. 58, No. 18, pp. 341-348
DOI: 10.1016/j.ifacol.2024.09.053
arXiv: 2404.09566

Schiller, J. D.; Grüne, L. & Müller, M. A. (2024): Optimal state estimation: Turnpike analysis and performance results,  Submitted
arXiv: 2409.14873

Alsalti, M.; Barkey, M.; Lopez, V. G. & Müller, M. A. (2023): Sample- and computationally efficient data-driven predictive control,  submitted
arXiv: 2309.11238

Alsalti, M.; Lopez, V. G. & Müller, M. A. (2023): On the design of persistently exciting inputs for data-driven control of linear and nonlinear systems,  IEEE Control Systems Letters, vol. 7, pp. 2629 - 2634
DOI: 10.1109/LCSYS.2023.3287133
arXiv: 2303.08707

Alsalti, M.; Lopez, V. G. & Müller, M. A. (2023): Notes on data-driven output-feedback control of linear MIMO systems,  submitted
arXiv: 2311.17484

Alsalti, M.; Lopez, V. G. & Müller, M. A. (2023): An efficient data-based off-policy Q-learning algorithm for optimal output feedback control of linear systems,  submitted
arXiv: 2312.03451

Alsalti, M.; Lopez, V. G; Berberich, J.; Allgöwer, F. & Müller, M. A. (2023): Data-Based Control of Feedback Linearizable Systems,  IEEE Transactions on Automatic Control (Early Access)
DOI: 10.1109/TAC.2023.3249289
arXiv: 2204.01148

Alsalti, M.; Markovsky, I.; Lopez, V. G. & Müller, M. A. (2023): Data-based system representations from irregularly measured data,  submitted
arXiv: 2307.11589

Knüfer, S. & Müller, M. A. (2023): Nonlinear Full Information and Moving Horizon Estimation: Robust Global Asymptotic Stability,  Automatica, Vol. 150, p. 110603
DOI: 10.1016/j.automatica.2022.110603

Krauss, I.; Lopez, V. G. & Müller, M. A. (2023): Sample-based nonlinear detectability for discrete-time systems,  submitted
arXiv: 2312.13658

Lopez, V. G.; Alsalti, M. & Müller, M. A. (2023): Efficient Off-Policy Q-Learning for Data-Based Discrete-Time LQR Problems,  IEEE Transactions on Automatic Control, pp. 1-12
DOI: 10.1109/TAC.2023.3235967

Markovsky, I.; Alsalti, M. ; Lopez, V. G. & Müller, M. A. (2023): Identification from data with periodically missing output samples,  submitted
DOI: 10.15488/15821

Nonhoff, M. & Müller, M. A. (2023): On the relation between dynamic regret and closed-loop stability,  Systems & Control Letters, vol. 177, p. 105532
DOI: 10.1016/j.sysconle.2023.105532
arXiv: 2209.05964

Schiller, J. D. & Müller, M. A. (2023): Suboptimal nonlinear moving horizon estimation,  IEEE Transactions on Automatic Control, Vol. 68, No. 4, pp. 2199-2214
DOI: 10.1109/TAC.2022.3173937
arXiv: 2108.13750

Schiller, J. D. & Müller, M. A. (2023): On an integral variant of incremental input/output-to-state stability and its use as a notion of nonlinear detectability,  IEEE Control Systems Letters, Vol. 7, pp. 2341-2346
DOI: 10.1109/LCSYS.2023.3286174
arXiv: 2305.05442

Schiller, J. D. & Müller, M. A. (2023): Nonlinear moving horizon estimation for robust state and parameter estimation,  Submitted
arXiv: 2312.13175

Schiller, J. D.; Muntwiler, S.; Köhler, J.; Zeilinger, M. N. & Müller, M. A. (2023): A Lyapunov function for robust stability of moving horizon estimation,  IEEE Transactions on Automatic Control, Vol. 68, Issue 12, pp. 7466-7481
DOI: 10.1109/TAC.2023.3280344
arXiv: 2202.12744

Schiller, J. D.; Wu, B. & Müller, M. A. (2023): A simple suboptimal moving horizon estimation scheme with guaranteed robust stability,  IEEE Control Systems Letters, Vol. 7, pp. 19-24
DOI: 10.1109/LCSYS.2022.3186236
arXiv: 2203.16090

Schwenkel, L.; Koehler, J.; Müller, M. A. & Allgöwer, F. (2023): Model predictive control for linear uncertain systems using integral quadratic constraints,  IEEE Transactions on Automatic Control, Vol. 68, Issue 1
DOI: 10.1109/TAC.2022.3171410

Soloperto, R.; Müller, M. A. & Allgöwer, F. (2023): Guaranteed closed-loop learning in Model Predictive Control,  IEEE Transactions on Automatic Control, Vol. 68, Issue 2
DOI: 10.1109/TAC.2022.3172453

Becker, M.; Köhler, J.; Haddadin, S. & Müller, M. A. (2022): Motion Planning using Reactive Circular Fields: A 2D Analysis of Collision Avoidance and Goal Convergence,  submitted
arXiv: 2210.16106

Berberich, J.; Köhler, J.; Müller, M. A. & Allgöwer, F. (2022): Linear tracking MPC for nonlinear systems part I: the model-based case,  IEEE Trans. Automat. Control, Vol. 67, Issue 9, pp. 4390-4405
DOI: 10.1109/TAC.2022.3166872
arXiv: 2105.08560

Berberich, J.; Köhler, J.; Müller, M. A. & Allgöwer, F. (2022): Linear tracking MPC for nonlinear systems part II: the data-driven case,  IEEE Trans. Automat. Control, Vol. 67, Issue 9, pp. 4406-4421
DOI: 10.1109/TAC.2022.3166851
arXiv: 2105.08567

Grüne, L.; Krügel, L. & Müller, M. A. (2022): Multiobjective strict dissipativity via a weighted sum approach,  Systems & Control Letters, Vol. 170, p. 105396
DOI: 10.1016/j.sysconle.2022.105396

Kleyman, V.; Schaller, M.; Mordmüller, M.; Wilson, M.; Brinkmann, R.; Worthmann, K. & Müller, M. A. (2022): State and Parameter Estimation for Retinal Laser Treatment,  IEEE Transactions on Control Systems Technology, pp. 1-13
DOI: 10.1109/TCST.2022.3228442
arXiv: 2203.12452

Klöppelt, C.; Berberich, J.; Allgöwer, F. & Müller, M. A. (2022): A novel constraint-tightening approach for robust data-driven predictive control,  Int J Robust Nonlinear Control, pp. 1-22
DOI: 10.1002/rnc.6532

Köhler, J.; Müller, M. A. & Allgöwer, F. (2022): Constrained nonlinear output regulation using model predictive control,  IEEE Transactions on Automatic Control, Vol. 67, Issue 5, pp. 2419-2434
DOI: 10.1109/TAC.2021.3081080

Köhler, P.; Müller, M. A. & Allgöwer, F. (2022): Approximate dissipativity of cost-interconnected systems in distributed economic MPC,  IEEE Transactions on Automatic Control
DOI: 10.1109/TAC.2022.3173028

Nonhoff, M. & Müller, M. A. (2022): Online Convex Optimization for Data-Driven Control of Dynamical Systems,  IEEE Open Journal of Control Systems, vol. 1, pp. 180-193
DOI: 10.1109/OJCSYS.2022.3200021

Schaller, M.; Kleyman, V.; Mordmüller, M.; Schmidt, C.; Wilson, M.; Brinkmann, R.; Müller, M. A. & Worthmann, K. (2022): Model predictive control for retinal laser treatment at 1 kHz,  at - Automatisierungstechnik, Vol. 70, No. 11, pp. 992-1002
DOI: 10.1515/auto-2022-0030

Schaller, M.; Wilson, M.; Kleyman, V.; Mordmüller, M.; Brinkmann, R.; Müller, M. A. & Worthmann, K. (2022): Parameter estimation and model reduction for model predictive control in retinal laser treatment,  Control Engineering Practice, Vol. 128, p. 105320
DOI: 10.1016/j.conengprac.2022.105320

Villanueva, M. E.; Müller, M. A. & Houska, B. (2022): Configuration-Constrained Tube MPC,  submitted
arXiv: 2208.12554

Wolff, T. M.; Menzel, M.; Dietrich, J. W. & Müller, M. A. (2022): Treating Hyperthyroidism: Model Predictive Control for the Prescription of Antithyroid Agents,  submitted
arXiv: 2212.10096

Wolff, T. M.; Veil, C.; Dietrich, J. W. & Müller, M. A. (2022): Mathematical Modeling and Simulation of Thyroid Homeostasis: Implications for the Allan-Herndon-Dudley Syndrome,  Frontiers in Endocrinology, Vol. 13, No. 882788
DOI: 10.3389/fendo.2022.882788

Wolff, T.; Dietrich, J. W. & Müller, M. A. (2022): Optimal hormone replacement therapy in hypothyroidism - a model predictive control approach,  Frontiers in Endocrinology, 13: 884018
DOI: 10.3389/fendo.2022.884018

Wolff, T.; Lopez, V. G. & Müller, M. A. (2022): Robust Data-Driven Moving Horizon Estimation for Linear Discrete-Time Systems,  submitted
arXiv: 2210.09017

Becker, M.; Lilge, T.; Müller, M. A. & Haddadin, S. (2021): Circular Fields and Predictive Multi-Agents for Online Global Trajectory Planning,  IEEE Robotics and Automation Letters, 2021, 6, 2618-2625
DOI: 10.1109/LRA.2021.3061997

Berberich, J.; Köhler, J.; Müller, M. A. & Allgöwer, F. (2021): Data-Driven Model Predictive Control With Stability and Robustness Guarantees,  IEEE Transactions on Automatic Control, 2021, Vol. 66, No. 4, pp. 1702-1717
DOI: 10.1109/TAC.2020.3000182

Berberich, J.; Köhler, J.; Müller, M. A. & Allgöwer, F. (2021): Data-driven model predictive control: closed-loop guarantees and experimental results,  at–Automatisierungstechnik, vol 69
DOI: 10.1515/auto-2021-0024

Grüne, L.; Müller, M. A.; Kellett, C. M. & Weller, S. (2021): Strict dissipativity for discrete time discounted optimal control problems,  Mathematical Control and Related Fields, Vol. 11, Iss. 4, pp. 771-796
DOI: 10.3934/mcrf.2020046

Köhler, P. N.; Müller, M. A.; Pannek, J. & Allgöwer, F. (2021): Distributed economic model predictive control for cooperative supply chain management using customer forecast information,  IFAC Journal of Systems and Control, Vol. 15, 100125
DOI: 10.1016/j.ifacsc.2020.100125

Mordmüller, M.; Kleyman, V.; Schaller, M.; Wilson, M.; Theisen-Kunde, D.; Worthmann, K.; Müller, M. A. & Brinkmann, R. (2021): Towards temperature controlled retinal laser treament with a single 10kHz laser,  Advanced Optical Technologies, Vol. 10, No. 6, pp. 423-431
DOI: 10.1515/aot-2021-0041

Berberich, J.; Köhler, J.; Allgöwer, F. & Müller, M. A. (2020): Dissipativity properties in constrained optimal control: a computational approach,  Automatica, vol. 114, Article 108840
DOI: 10.1016/j.automatica.2020.108840

Brändle, S.; Schmitt, S. & Müller, M. A. (2020): A systems-theoretic analysis of low-level human motor control: application to a single-joint arm model,  Journal of Mathematical Biology 80, 1139–1158
DOI: 10.1007/s00285-019-01455-z

Kleyman, V.; Gernandt, H.; Worthmann, K.; Abbas, H. S.; Brinkmann, R. & Müller, M. A. (2020): Modeling and parameter identification for real-time temperature controlled retinal laser therapies,  at - Automatisierungstechnik, 2020, 68, 953 - 966
DOI: 10.1515/auto-2020-0074

Köhler, J.; Kötting, P.; Soloperto, R.; Allgöwer, F. & Müller, M. A. (2020): A robust adaptive model predictive control framework for nonlinear uncertain systems,  International Journal of Robust and Nonlinear Control, Vol. 31, Issue 18, pp. 8725-8749
DOI: 10.1002/rnc.5147

Köhler, J.; Müller, M. A. & Allgöwer, F. (2020): A nonlinear model predictive control framework using reference generic terminal ingredients,  IEEE Transactions on Automatic Control, 2020, 65, 3576-3583
DOI: 10.1109/TAC.2019.2949350

Köhler, J.; Müller, M. A. & Allgöwer, F. (2020): A nonlinear tracking model predictive control scheme for dynamic target Signals,  Automatica Vol. 118, Aritcle 109030
DOI: 10.1016/j.automatica.2020.109030

Köhler, J.; Müller, M. A. & Allgöwer, F. (2020): Periodic optimal control of nonlinear constrained systems using economic model predictive control,  Journal of Process Control, Vol. 92, pp. 185-201
DOI: 10.1016/j.jprocont.2020.06.004

Köhler, J.; Soloperto, R.; Müller, M. A. & Allgöwer, F. (2020): A computationally efficient robust model predictive control framework for uncertain nonlinear systems,  IEEE Transactions on Automatic Control
DOI: 10.1109/TAC.2020.2982585

Pozzato, G.; Müller, M. A.; Formentin, S. & Savaresi, S. M. (2020): Economic MPC for online least costly energy management of hybrid electric vehicles,  Control Engineering Practice, Vol.102, Article 104534
DOI: 10.1016/j.conengprac.2020.104534

Villanueva, M. E.; De Lazzari, E.; Müller, M. A. & Houska, B. (2020): A set-theoretic generalization of dissipativity with applications in tube MPC,  Automatica, 2020, 122, 109179
DOI: 10.1016/j.automatica.2020.109179

Köhler, J.; Müller, M. A. & Allgöwer, F. (2019): Nonlinear reference tracking: An economic model predictive control perspective,  IEEE Trans. Automat. Control, vol. 64, no. 1, pp. 254-269.
DOI: 10.1109/TAC.2018.2800789

Köhler, J.; Müller, M. A. & Allgöwer, F. (2019): Distributed model predictive control - Recursive feasibility under inexact dual optimization,  Automatica, vol. 102, pp. 1-9
DOI: 10.1016/j.automatica.2018.12.037

Wildhagen, S.; Müller, M. A. & Allgöwer, F. (2019): Predictive Control Over a Dynamical Token Bucket Network,  IEEE Control Systems Letters, vol. 3, no. 4, pp 859-864
DOI: 10.1109/LCSYS.2019.2919264

Bayer, Florian A.; Müller, Matthias A. & Allgöwer, Frank (2018): On optimal system operation in robust economic MPC,  Automatica, vol. 88, pp. 98 - 106.
DOI: 10.1016/j.automatica.2017.11.007

Berberich, J.; Dietrich, J. W.; Hoermann, R. & Müller, M. A. (2018): Mathematical modeling of the pituitary-thyroid feedback loop: role of a TSH-T3-shunt and sensitivity analysis,  Frontiers in Endocrinology, vol. 9, pp. 91.
DOI: 10.3389/fendo.2018.00091

Berberich, J.; Köhler, J.; Allgöwer, F. & Müller, M. A. (2018): Indefinite Linear Quadratic Optimal Control: Strict Dissipativity and Turnpike Properties,  IEEE Control Systems Letters, vol. 2, no. 3, pp. 399-404.
DOI: 10.1109/LCSYS.2018.2842142

Brunner, F. D.; Müller, M. A. & Allgöwer, F. (2018): Enhancing Output-feedback MPC with Set-valued Moving Horizon Estimation,  IEEE Transactions on Automatic Control, vol. 63, no. 9, pp. 2976-2986.
DOI: 10.1109/TAC.2018.2791899

Faulwasser, T.; Grüne, L. & Müller, M. A. (2018): Economic Nonlinear Model Predictive Control,  Foundations and Trends in Systems and Control,, vol. 5, no. 1, pp. 1-98.
DOI: 10.1561/2600000014

Köhler, J.; Müller, M. A. & Allgöwer, F. (2018): On periodic dissipativity notions in economic model predictive control,  IEEE Control Systems Letters, vol. 2, no. 3, pp. 501-506.
DOI: 10.1109/LCSYS.2018.2842426

Köhler, P. N. ; Müller, M. A. & Allgöwer, F. (2018): A distributed economic MPC framework for cooperative control under conflicting objectives,  Automatica, 96, 368 - 379
DOI: 10.1016/j.automatica.2018.07.001

Wiedemann, L. G.; Jayaneththi, V. R.; Kimpton, J.; Chan, A.; Müller, M. A.; Hogan, A.; Lim, E.; Wilson, N. C. & McDaid, A. J. (2018): Neuromuscular characterisation in Cerebral Palsy using hybrid Hill-type models on isometric contractions,  Computers in Biology and Medicine, vol. 103, pp. 269-276.
DOI: 10.1016/j.compbiomed.2018.10.027

Houska, B. & Müller, M. A. (2017): Cost-to-travel functions: a new perspective on optimal and model predictive control,  Syst. Contr. Lett., vol. 106, pp. 79-86.
DOI: 10.1016/j.sysconle.2017.06.005

Lorenzen, M.; Müller, M. A. & Allgöwer, F. (2017): Stochastic Model Predictive Control without Terminal Constraints,  Int. J. Robust and Nonlinear Control.
DOI: 10.1002/rnc.3912

Müller, M. A. & Allgöwer, F. (2017): Economic and distributed model predictive control: recent developments in optimization-based control,  SICE Journal of Control, Measurement, and System Integration, vol. 10, no. 2, pp. 39-52.
DOI: 10.9746/jcmsi.10.39

Müller, Matthias A. (2017): Nonlinear moving horizon estimation in the presence of bounded disturbances,  Automatica, vol. 79, pp. 306 - 314.
DOI: 10.1016/j.automatica.2017.01.033

Müller, Matthias A. & Worthmann, Karl (2017): Quadratic costs do not always work in MPC,  Automatica, vol. 82, pp. 269 - 277.
DOI: 10.1016/j.automatica.2017.04.058

Bayer, F. A.; Lorenzen, M.; Müller, M. A. & Allgöwer, F. (2016): Robust Economic Model Predictive Control using Stochastic Information,  Automatica, vol. 74, pp. 151-161.
DOI: 10.1016/j.automatica.2016.08.008

Grüne, L. & Müller, M. A. (2016): On the relation between strict dissipativity and turnpike properties,  System & Control Letters, vol. 90, pp. 45-53 (Brockett-Willems Outstanding Paper Award for the best paper published in Systems & Control Letters in the period 2014-2018).
DOI: 10.1016/j.sysconle.2016.01.003

Müller, M. A. & Grüne, L. (2016): Economic model predictive control without terminal constraints for optimal periodic behavior,  Automatica, vol. 70, pp. 128-139.
DOI: 10.1016/j.automatica.2016.03.024

Müller, M. A.; Angeli, D. & Allgöwer, F. (2015): On necessity and robustness of dissipativity in economic model predictive control,  IEEE Trans. Automat. Control, vol. 60, no. 6, pp. 1671-1676.
DOI: 10.1109/TAC.2014.2361193

Müller, M. A.; Liberzon, D. & Allgöwer, F. (2015): Norm-controllability of nonlinear systems,  IEEE Trans. Automat. Control, vol. 60, no. 7, pp. 1825-1840.
DOI: 10.1109/TAC.2015.2394953

Bayer, F.; Müller, M. A. & Allgöwer, F. (2014): Tube-based Robust Economic Model Predictive Control,  J. Proc. Contr., vol. 24, no. 8, pp. 1237-1246.
DOI: 10.1016/j.jprocont.2014.06.006

Müller, M. A.; Angeli, D. & Allgöwer, F. (2014): On the performance of economic model predictive control with self-tuning terminal cost,  J. Proc. Contr., vol. 24, no. 8, pp. 1179-1186.
DOI: 10.1016/j.jprocont.2014.05.009

Müller, M. A.; Angeli, D. & Allgöwer, F. (2014): Transient average constraints in economic model predictive control,  Automatica, vol. 50, no. 11, pp. 2943-2950.
DOI: 10.1016/j.automatica.2014.10.024

Müller, M. A.; Angeli, D.; Allgöwer, F.; Amrit, R. & Rawlings, J. B. (2014): Convergence in economic model predictive control with average constraints,  Automatica, vol. 50, no. 12, pp. 3100-3111.
DOI: 10.1016/j.automatica.2014.10.059

Müller, M. A.; Angeli, D. & Allgöwer, F. (2013): Economic model predictive control with self-tuning terminal cost,  European J. Control, vol. 19, no. 5, pp. 408-416.
DOI: 10.1016/j.ejcon.2013.05.019

Müller, M. A. & Allgöwer, F. (2012): Improving performance in model predictive control: Switching cost functionals under average dwell-time,  Automatica, vol. 48, no. 2, pp. 402-409.
DOI: 10.1016/j.automatica.2011.11.005

Müller, M. A. & Dominguez-Garcia, A. D. (2012): Fault coverage modeling in nonlinear dynamical systems,  Automatica, vol. 48, no. 7, pp. 1372-1379.
DOI: 10.1016/j.automatica.2012.04.007

Müller, M. A. & Liberzon, D. (2012): Input/output-to-state stability and state-norm estimators for switched nonlinear systems,  Automatica, vol. 48, no. 9, pp. 2029-2039.
DOI: 10.1016/j.automatica.2012.06.026

Müller, M. A.; Martius, P. & Allgöwer, F. (2012): Model predictive control of switched nonlinear systems under average dwell-time,  J. Proc. Contr., vol. 22, no. 9, pp. 1702-1710.
DOI: 10.1016/j.jprocont.2012.07.004

Müller, M. A.; Reble, M. & Allgöwer, F. (2012): Cooperative control of dynamically decoupled systems via distributed model predictive control,  Int. J. Robust and Nonlinear Control, vol. 22, no. 12, pp. 1376-1397.
DOI: 10.1002/rnc.2826

Buschermöhle, P.; Jouini, T.; Lilge, T. & Müller, M. A. (2024): Disturbance feedback-based model predictive control in uncertain dynamic environments,  Accepted at Conference on Nonlinear Model Predictive Control (NMPC 2024)
arXiv: 2404.09893

Alsalti, M.; Lopez, V. G.; Berberich, J.; Allgöwer, F. & Müller, M. A. (2023): Data-driven nonlinear predictive control for feedback linearizable systems,  22nd IFAC World Congress: Yokohama, Japan, July 9-14, 2023. IFAC-PapersOnLine, Vol. 56, Issue 2, pp. 617-624
DOI: 10.1016/j.ifacol.2023.10.1636
arXiv: 2211.06339

Becker, M.; Caspers, P.; Hattendorf, T.; Lilge, T.; Haddadin, S. & Müller, M. A. (2023): Informed Circular Fields for Global Reactive Obstacle Avoidance of Robotic Manipulators,  22nd IFAC World Congress: Yokohama, Japan, July 9-14, 2023. IFAC-PapersOnLine, Vol. 56, Issue 2, pp. 1017-1022
DOI: 10.1016/j.ifacol.2023.10.1698
arXiv: 2212.05815

Buschermöhle, P.; Lilge, T. & Müller, M. A. (2023): Robust model predictive control for linear sampled-data systems with irregular sampling times,  22nd IFAC World Congress: Yokohama, Japan, July 9-14, 2023. IFAC-PapersOnLine, Vol. 56, Issue 2, pp. 9399-9404
DOI: 10.1016/j.ifacol.2023.10.231

Lopez, V. G. & Müller, M. A. (2023): An efficient off-policy reinforcement learning algorithm for the continuous-time LQR problem,  Accepted for IEEE 62nd Conference on Decision and Control (CDC)
arXiv: 2303.17819

Menzel, M.; Wolff, T. M.; Dietrich, J. W. & Müller, M. A. (2023): Model predictive control for the prescription of antithyroid agents,  Proceedings on Automation in Medical Engineering More info
arXiv: 2307.16690

Nonhoff, M.; Köhler, J. & Müller, M. A. (2023): Online convex optimization for constrained control of linear systems using a reference governor,  22nd IFAC World Congress: Yokohama, Japan, July 9-14, 2023. IFAC PapersOnLine, Vol. 56, Issue 2, pp 2570-2575
DOI: 10.1016/j.ifacol.2023.10.1340
arXiv: 2211.09088

Schiller, J. D. & Müller, M. A. (2023): A moving horizon state and parameter estimation scheme with guaranteed robust convergence,  22nd IFAC World Congress: Yokohama, Japan, July 9-14, 2023. IFAC-PapersOnLine, Vol. 56, Issue 2, pp. 6759-6764
DOI: 10.1016/j.ifacol.2023.10.382
arXiv: 2211.09053

Wolff, T. M. & Lopez, V. G. & Müller, M. A. (2023): Robust Stability of Gaussian Process Based Moving Horizon Estimation,  2023 IEEE 62nd Conference on Decision and Control (CDC), pp. 4087-4093
DOI: 10.1109/CDC49753.2023.10383304
arXiv: 2304.06530

Berberich, J.; Köhler, J.; Müller, M. A. & Allgöwer, F. (2022): Stability in data-driven MPC: an inherent robustness perspective,  2022 IEEE 61st Conference on Decision and Control (CDC), pp. 1105-1110
DOI: 10.1109/CDC51059.2022.9993361

Köhler, M.; Berberich, J.; Müller, M. A. & Allgöwer, F. (2022): Data-driven distributed MPC of dynamically coupled linear systems,  IFAC-PapersOnLine, Vol. 55, Issue 30, pp. 365-370, 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2022)
DOI: 10.1016/j.ifacol.2022.11.080

Krauss, I.; Lopez, V. G. & Müller, M. A. (2022): Sample-based observability of linear discrete-time systems,  2022 IEEE 61st Conference on Decision and Control (CDC)
DOI: 10.1109/CDC51059.2022.9993267

Lopez, V. G. & Müller, M. A. (2022): On a continuous-time version of Willems' lemma,  IEEE 61st Conference on Decision and Control (CDC), pp. 2759-2764
DOI: 10.1109/CDC51059.2022.9992347

Wolff, T. M.; Lopez, V. G. & Müller, M. A. (2022): Data-Based Moving Horizon Estimation for Linear Discrete-Time Systems,  2022 European Control Conference (ECC), pp. 1778-1783
DOI: 10.23919/ecc55457.2022.9838331
arXiv: 2111.04979

Alsalti, M.; Berberich, J.; Lopez, V. G.; Allgöwer, F. & Müller, M. A. (2021): Data-Based System Analysis and Control of Flat Nonlinear Systems,  60th IEEE Conference on Decision and Control (CDC), pp. 1484-1489
DOI: 10.1109/CDC45484.2021.9683327
arXiv: 2103.02892

Berberich, J.; Köhler, J.; Müller, M. A. & Allgöwer, F. (2021): On the design of terminal ingredients for data-driven MPC,  IFAC-PapersOnLine, Volume 54, Issue 6, Pages 257-263. Proc. of the 7th IFAC Conference on Nonlinear Model Predictive Control (NMPC)
DOI: 10.1016/j.ifacol.2021.08.554

Kleyman, V.; Schaller, M; Wilson, M; Mordmüller, M.; Brinkmann, R.; Worthmann, K. & Müller, M. A. (2021): State and parameter estimation for model-based retinal laser treatment,  7th IFAC Conference on Nonlinear Model Predictive Control NMPC 2021, IFAC-PapersOnLine, Vol. 54, Issue 6, pp. 244-250
DOI: 10.1016/j.ifacol.2021.08.552
arXiv: 2103.03189

Klöppelt, C.; Schwenkel, L.; Allgöwer, F. & Müller, M. A. (2021): Transient Performance of Tube-based Robust Economic Model Predictive Control,  7th IFAC Conference on Nonlinear Model Predictive Control NMPC 2021, IFAC-PapersOnline, Vol. 54, Issue 6, pp. 28-35
DOI: 10.1016/j.ifacol.2021.08.520
arXiv: 2102.09404

Nonhoff, M. & Müller, M. A. (2021): Data-driven online convex optimization for control of dynamical systems,  2021 60th IEEE Conference on Decision and Control (CDC), 2021, 3640-3645
DOI: 10.1109/CDC45484.2021.9683550
arXiv: 2103.09127

Nonhoff, M. & Müller, M. A. (2021): An online convex optimization algorithm for controlling linear systems with state and input constraints,  2021 American Control Conference (ACC), pp. 2523-2528
DOI: 10.23919/ACC50511.2021.9482877
arXiv: 2005.11308

Schiller, J. D.; Knüfer, S. & Müller, M. A. (2021): Robust Stability of Suboptimal Moving Horizon Estimation using an Observer-Based Candidate Solution,  IFAC-PapersOnLine, Vol. 54, Issue 6, pp. 226-231
DOI: 10.1016/j.ifacol.2021.08.549

Berberich, J.; Köhler, J.; Müller, M. A. & Allgöwer, F. (2020): Robust Constraint Satisfaction in Data-Driven MPC,  In Proceedings of the 59th IEEE Conference on Decision and Control (CDC), pp. 1260-1267 (Outstanding Student Paper Award).
DOI: 10.1109/CDC42340.2020.9303965

Camisa, A.; Köhler, P. N.; Müller, M. A.; Notarstefano, G. & Allgöwer, F. (2020): A distributed optimization algorithm for Nash bargaining in multi-agent systems,  21st IFAC World Congress, Vol. 53, Issue 2, pp. 2684-2689
DOI: 10.1016/j.ifacol.2020.12.402

Hirche, M.; Köhler, P. N.; Müller, M. A. & Allgöwer, F. (2020): Distributed Model Predictive Control for Consensus of Constrained Heterogeneous Linear Systems,  In Proceedings of 59th IEEE Conference on Decision and Control (CDC), pp. 1248-1253
DOI: 10.1109/CDC42340.2020.9303838

J. Köhler, M. A. Müller and F. Allgöwer (2020): Implicit solutions to constrained nonlinear output regulation using MPC,  In Proceedings of 59th IEEE Conference on Decision and Control (CDC), pp. 4604-4609
DOI: 10.1109/CDC42340.2020.9303983

Julian Berberich, Johannes Köhler, Matthias A. Müller, Frank Allgöwer (2020): Data-Driven Tracking MPC for Changing Setpoints,  21st IFAC World Congress, Vol. 53, Issue 2, pp. 6923-6930
DOI: 10.1016/j.ifacol.2020.12.389

Knüfer, S. & Müller, M. A. (2020): Time-Discounted Incremental Input/Output-to-State Stability,  In Proceedings of 59th IEEE Conference on Decision and Control (CDC), pp. 5394-5400
DOI: 10.1109/CDC42340.2020.9304034

Nonhoff, M. & Müller, M. A. (2020): Online Gradient Descent for Linear Dynamical Systems,  21st IFAC World Congress, Vol. 53, Issue 2, pp. 945-952
DOI: 10.1016/j.ifacol.2020.12.1258

Pavlov, A.; Müller, M. A.; Manzie, C. & Shames I. (2020): Complexity minimisation of suboptimal MPC without terminal constraints,  21st IFAC World Congress, Vol. 53, Issue 2, pp. 6089-6094
DOI: 10.1016/j.ifacol.2020.12.1682

Pozzato, G.; Müller, M. A.; Formentin, S. & Savaresi, S. (2020): On Strict Dissipativity of Systems Modeled by Convex Difference Inclusions: Theory and Application to Hybrid Electric Vehicles,  21st IFAC World Congress, Vol. 53, Issue 2, pp. 14211-14216
DOI: 10.1016/j.ifacol.2020.12.1058

Schwenkel, L.; Köhler, J.; Müller, M. A. & Allgöwer, F. (2020): Robust Economic Model Predictive Control without Terminal Conditions,  21st IFAC World Congress, Vol. 53, Issue 2, pp. 7097-7104
DOI: 10.1016/j.ifacol.2020.12.465

Schwenkel, L.; Köhler, J.; Müller, M. A. & Allgöwer, F. (2020): Dynamic uncertainties in model predictive control: Guaranteed stability for constrained linear systems,  2020 In Proceedings of the 59th IEEE Conference on Decision and Control (CDC)
DOI: 10.1109/CDC42340.2020.9303819

Köhler, J.; Müller, M. A. & Allgöwer, F. (2019): A simple framework for nonlinear robust output-feedback MPC,  18th European Control Conference (ECC 2019), pp. 793-798
DOI: 10.23919/ECC.2019.8795965

Köhler, Johannes; Andina, Elisa; Soloperto, Raffaele, Müller, Matthias A. & Allgöwer, Frank (2019): Linear robust adaptive model predictive control: Computational complexity and conservatism,  58th IEEE Conference on Decision and Control (CDC)
DOI: 10.1109/CDC40024.2019.9028970

Köhler, P. N.; Müller, M. A. & Allgöwer, F. (2019): Approximate dissipativity and performance bounds for interconnected systems,  18th European Control Conference (ECC 2019), pp. 787-792
DOI: 10.23919/ECC.2019.8796123

Köhler, P. N.; Müller, M. A. & Allgöwer, F. (2019): Graph topology and subsystem centrality in approximately dissipative system interconnections,  58th IEEE Conference on Decision and Control (CDC)
DOI: 10.1109/CDC40024.2019.9029400

Linsenmeyer, Steffen & Müller, Matthias A. & Ishii, Hideaki & Allgöwer, Frank (2019): Event-based Containability for Linear Systems with Arbitrarily Small Bit Rates,  8th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys), Vol. 52, Issue 20, pp. 31-36
DOI: 10.1016/j.ifacol.2019.12.138

Neureuther, Philip L.; Ederer, Michael; Selig, Peter & Müller, Matthias A. (2019): Modeling of a RF Surgical Generator based on a Push-Pull Oscillator,  Joint 8th IFAC Symposium on Mechatronic Systems and 11th IFAC Symposium on Nonlinear Control Systems, Vol. 52, Issue 15, pp. 382-387
DOI: 10.1016/j.ifacol.2019.11.705

Soloperto, R.; Köhler; J.; Müller; M. A. & Allgöwer, F. (2019): Collision avoidance for uncertain nonlinear systems and moving obstacles using robust Model Predictive Control,  18th European Control Conference (ECC 2019), pp. 811-817
DOI: 10.23919/ECC.2019.8796049

Soloperto, Raffaele; Köhler, Johannes; Müller, Matthias A. & Allgöwer, Frank (2019): Dual Adaptive MPC for output tracking of linear systems,  58th IEEE Conference on Decision and Control (CDC)
DOI: 10.1109/CDC40024.2019.9029693

Wildhagen, Stefan; Müller, Matthias A. & Allgöwer, Frank (2019): Economic MPC using a Cyclic Horizon with Application to Networked Control Systems,  Joint 8th IFAC Symposium on Mechatronic Systems and 11th IFAC Symposium on Nonlinear Control Systems, Vol. 52, Issue 16, pp. 502-507
DOI: 10.1016/j.ifacol.2019.12.011

Knüfer, S. & Müller, M. A. (2018): Robust Global Exponential Stability for Moving Horizon Estimation,  Proc. 57th IEEE Conf. Decision and Control (CDC), pp. 3477-3482.
DOI: 10.1109/CDC.2018.8619617

Köhler, J.; Müller, M. A. & Allgöwer, F. (2018): Nonlinear Reference Tracking with Model Predictive Control: An Intuitive Approach,  Proc. European Control Conf. (ECC), Limassol, Cyprus, pp. 1355-1360.
DOI: 10.23919/ECC.2018.8550428

Köhler, J.; Müller, M. A. & Allgöwer, F. (2018): A novel constraint tightening approach for nonlinear robust model predictive control,  Proc. American Control Conf. (ACC), Milwaukee, Wisconsin, pp. 728-734.
DOI: 10.23919/ACC.2018.8431892

Köhler, J.; Müller, M. A. & Allgöwer, F. (2018): MPC for nonlinear periodic tracking using reference generic offine computations,  Proc. IFAC Conf. Nonlinear Model Predictive Control (NMPC), vol. 51, no. 20, Madison, Wisconsin, pp. 556-561.
DOI: 10.1016/j.ifacol.2018.11.032

Köhler, P. N. & Müller, M. A. & Allgöwer, F. (2018): Interconnections of dissipative systems and distributed economic MPC,  IFAC-PapersOnLine, vol. 51, no. 20, 86-91 (6th IFAC Conference on Nonlinear Model Predictive Control NMPC 2018)
DOI: 10.1016/j.ifacol.2018.10.179

Soloperto, Raffaele; Müller, Matthias A.; Trimpe, Sebastian & Allgöwer, Frank (2018): Learning-Based Robust Model Predictive Control with State-Dependent Uncertainty,  IFAC-PapersOnLine, vol. 51, no. 20, 442 - 447 (6th IFAC Conference on Nonlinear Model Predictive Control NMPC 2018)
DOI: 10.1016/j.ifacol.2018.11.052

Wabersich, K. P.; Bayer, F. A.; Müller, M. A. & Allgöwer, F. (2018): Economic model predictive control for robust periodic operation with guaranteed closed-loop performance,  Proc. European Control Conf. (ECC), Limassol, Cyprus, pp. 507-513.
DOI: 10.23919/ECC.2018.8550262

Köhler, J.; Müller, M. A.; Li, Na & Allgöwer, F. (2017): Real Time Economic Dispatch for power networks: A Distributed Economic Model Predictive Control Approach,  Proc. 56th IEEE Conf. Decision and Control (CDC), Melbourne, Victoria, Australia, pp. 6340-6345.
DOI: 10.1109/CDC.2017.8264615

Köhler, P. N.; Müller, M. A. & Allgöwer, F. (2017): Transient performance of economic model predictive control with average constraints,  Proc. 56th IEEE Conf. Decision and Control (CDC), Melbourne, Victoria, Australia, pp. 5557-5562.
DOI: 10.1109/CDC.2017.8264484

Köhler, P. N.; Müller, M. A.; Pannek, J. & Allgöwer, F. (2017): On Exploitation of Supply Chain Properties by Sequential Distributed MPC.,  Proc. of the 20th IFAC World Congress, vol. 50, no. 1, Toulouse, France, pp. 7947-7952.
DOI: 10.1016/j.ifacol.2017.08.706

Lorenzen, Matthias; Müller, Matthias A. & Allgöwer, Frank (2017): Stabilizing Stochastic MPC without Terminal Constraints,  Proc. American Control Conf. (ACC), 04, Seattle, Washington, pp. 5636-5641.
DOI: 10.23919/ACC.2017.7963832

Müller, M. A. & Grüne, L. (2017): On the relation between dissipativity and discounted dissipativity,  Proc. 56th IEEE Conf. Decision and Control (CDC), pp. 5570-5575.
DOI: 10.1109/CDC.2017.8264486

Aydiner, E.; Müller, M. A. & Allgöwer, F. (2016): Periodic Reference Tracking for Nonlinear Systems via Model Predictive Control,  Proc. European Control Conf. (ECC), Aalborg, Denmark, pp. 2602-2607.
DOI: 10.1109/ECC.2016.7810682

Bayer, F. A.; Müller, M. A. & Allgöwer, F. (2016): Min-max Economic Model Predictive Control Approaches with Guaranteed Performance,  Proc. 55th IEEE Conf. Decision and Control (CDC), pp. 3210-3215.
DOI: 10.1109/CDC.2016.7798751

Brunner, F. D.; Müller, M. A. & Allgöwer, F. (2016): Enhancing Output Feedback MPC for Linear Discrete-time Systems with Set-valued Moving Horizon Estimation,  Proc. 55th IEEE Conf. Decision and Control (CDC), Las Vegas, NV, USA, pp. 2733-2738.
DOI: 10.1109/CDC.2016.7798675

Knüfer, S.; Müller, M. A. & Allgöwer, F. (2016): Stabilizing Model Predictive Control without Terminal Constraints for Switched Nonlinear Systems,  Proc. 10th IFAC Symp. Nonlinear Control Systems (NOLCOS), vol. 49, no. 18, Monterey, CA, USA, pp. 65-70.
DOI: 10.1016/j.ifacol.2016.10.141

Köhler, P. N.; Müller, M. A. & Allgöwer, F. (2016): A distributed economic MPC scheme for coordination of self-interested systems,  Proc. American Control Conf. (ACC), Boston, MA, USA, pp. 889-894.
DOI: 10.1109/ACC.2016.7525027

Müller, M. A. (2016): Nonlinear moving horizon estimation for systems with bounded disturbances,  Proc. American Control Conf. (ACC), Boston, MA, USA, pp. 883-888.
DOI: 10.1109/ACC.2016.7525026

Bayer, F. A.; Lorenzen, M.; Müller, M. A. & Allgöwer, F. (2015): Improving Performance in Robust Economic MPC Using Stochastic Information,  Proc. IFAC Conf. Nonlinear Model Predictive Control (NMPC), vol. 48, no. 23, Seville, Spain, pp. 410-415.
DOI: 10.1016/j.ifacol.2015.11.313

Bayer, F. A.; Müller, M. A. & Allgöwer, F. (2015): Average Constraints in Robust Economic Model Predictive Control,  Proc. IFAC Int. Symp. Advanced Control of Chemical Processes (ADCHEM), vol. 48, no. 8, Whistler, Britisch Columbia, Canada, pp. 44-49.
DOI: 10.1016/j.ifacol.2015.08.155

Müller, M. A. & Grüne, L. (2015): Economic model predictive control without terminal constraints: optimal periodic operation,  Proc. 54th IEEE Conf. Decision and Control (CDC), Osaka, Japan, pp. 4946-4951.
DOI: 10.1109/CDC.2015.7402992

Müller, M. A.; Grüne, L. & Allgöwer, F. (2015): On the role of dissipativity in economic model predictive control,  Proc. 5th IFAC Conf. Nonlinear Model Predictive Control (NMPC), vol. 48, no. 23, pp. 110-116 (Semi-Plenary Paper).
DOI: 10.1016/j.ifacol.2015.11.269

Bayer, F.; Müller, M. A. & Allgöwer, F. (2014): Set-based Disturbance Attenuation in Economic Model Predictive Control,  Proc. 19th IFAC World Congress, vol. 47, no. 3, Cape Town, South Africa, pp. 1898 - 1903.
DOI: 10.3182/20140824-6-ZA-1003.00951

Müller, M. A. & Allgöwer, F. (2014): Distributed economic MPC: a framework for cooperative control problems,  Proc. 19th IFAC World Congress, vol. 47, no. 3, Cape Town, South Africa, pp. 1029-1034.
DOI: 10.3182/20140824-6-ZA-1003.01177

Müller, M. A.; Angeli, D. & Allgöwer, F. (2014): Performance analysis of economic MPC with self-tuning terminal cost,  Proc. American Control Conf. (ACC), Portland, OR, USA, pp. 2845-2850.
DOI: 10.1109/ACC.2014.6858962

Schaich, R. M.; Müller, M. A. & Allgöwer, F. (2014): A distributed model predictive control scheme for networks with communication failure,  Proc. 19th IFAC World Congress, vol. 47, no. 3, Cape Town, South Africa, pp. 12004-12009.
DOI: 10.3182/20140824-6-ZA-1003.01507

Müller, M. A.; Angeli, D. & Allgöwer, F. (2013): On convergence of averagely constrained economic MPC and necessity of dissipativity for optimal steady-state operation,  Proc. American Control Conf. (ACC), Washington, DC, USA, pp. 3147-3152.
DOI: 10.1109/ACC.2013.6580314

Müller, M. A.; Angeli, D. & Allgöwer, F. (2013): Economic model predictive control with self-tuning terminal weight,  Proc. European Control Conf. (ECC), Zurich, Switzerland, pp. 2044-2049.
DOI: 10.23919/ECC.2013.6669271

Müller, M. A.; Angeli, D. & Allgöwer, F. (2013): Economic model predictive control with transient average constraints,  Proc. 52nd IEEE Conf. Decision and Control (CDC), Florence, Italy, pp. 5119-5124.
DOI: 10.1109/CDC.2013.6760693

Müller, M. A.; Liberzon, D. & Allgöwer, F. (2013): Norm-controllability, or how a nonlinear system responds to large inputs,  Proc. 9th IFAC Symp. Nonlinear Control Systems (NOLCOS), vol. 46, no. 23, pp. 104-109 (Semi-Plenary Paper).
DOI: 10.3182/20130904-3-FR-2041.00052

Müller, M. A. & Allgöwer, F. (2012): Robustness of steady-state optimality in economic model predictive control,  Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, pp. 1011-1016.
DOI: 10.1109/CDC.2012.6426754

Müller, M. A.; Liberzon, D. & Allgöwer, F. (2012): Relaxed conditions for norm-controllability of nonlinear systems,  Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, pp. 314-319.
DOI: 10.1109/CDC.2012.6426430

Müller, M. A.; Schürmann, B. & Allgöwer, F. (2012): Robust cooperative control of dynamically decoupled systems via distributed MPC,  Proc. IFAC Conf. Nonlinear Model Predictive Control (NMPC), vol. 45, no. 17, Noordwijkerhout, The Netherlands, pp. 412-417.
DOI: 10.3182/20120823-5-NL-3013.00007

Joos, A.; Müller, M. A.; Baumgärtner, D.; Fichter, W. & Allgöwer, F. (2011): Nonlinear Predictive Control Based on Time-Domain Simulation for Automatic Landing,  Proc. AIAA Guidance, Navigation, and Control Conf., vol. 2, Portland, OR, USA, pp. 1619-1633.
DOI: 10.2514/6.2011-6298

Müller, M. A. & Allgöwer, F. (2011): Model predictive control of switched nonlinear systems under average dwell-time,  Proc. American Control Conf. (ACC), San Francisco, CA, USA, pp. 5169-5174.
DOI: 10.1109/ACC.2011.5990955

Müller, M. A.; Liberzon, D. & Allgöwer, F. (2011): On norm-controllabilty of nonlinear systems,  Proc. 50th IEEE Conf. Decision and Control (CDC), European Control Conf. (ECC), Orlando, FL, USA, pp. 1741-1746.
DOI: 10.1109/CDC.2011.6160557

Müller, M. A.; Reble, M. & Allgöwer, F. (2011): A general distributed MPC framework for cooperative control,  Proc. 18th IFAC World Congress, vol. 44, no. 1, Milano, Italy, pp. 7987-7992.
DOI: 10.3182/20110828-6-IT-1002.02884

Reble, M.; Müller, M. A. & Allgöwer, F. (2011): Unconstrained Model Predictive Control and Suboptimality Estimates for Nonlinear Time-Delay Systems,  Proc. 50th IEEE Conf. Decision and Control (CDC), European Control Conf. (ECC), Orlando, FL, USA, pp. 7599-7604.
DOI: 10.1109/CDC.2011.6160266

Müller, M. A. & Domìnguez-Garcìa, A. D. (2010): On Input-to-State Stability Notions for Reachability Analysis of Power Systems,  Proc. IEEE Int. Symp. Circuits and Systems (ISCAS), Paris, France, pp. 2754-2757.
DOI: 10.1109/ISCAS.2010.5537027

Müller, M. A. & Liberzon, D. (2010): Input/output-to-state stability of switched nonlinear systems,  Proc. American Control Conf. (ACC), Baltimore, Maryland, USA, pp. 1708-1712.
DOI: 10.1109/ACC.2010.5531504

Müller, M. A. & Liberzon, D. (2010): State-norm estimators for switched nonlinear systems under average dwell-time,  Proc. 49th IEEE Conf. Decision and Control (CDC), Atlanta, GA, USA, pp. 1275-1280.
DOI: 10.1109/CDC.2010.5717596

Müller, M. A.; Waldherr, S. & Allgöwer, F. (2009): The transcritical bifurcation in absolutely stable feedback systems,  Proc. European Control Conf. (ECC), Budapest, Hungary, pp. 2146-2151.
DOI: 10.23919/ECC.2009.7074722

Angeli, D. & Müller, M. A. (2019): Economic Model Predictive Control: some design tools and analysis techniques,  In Rakovic, S. V. & Levine, W. S. (Ed.): Handbook of Model Predictive Control, Birkh'auser Basel, pp. 145-167.
DOI: 10.1007/978-3-319-77489-3_7

Grüne, L.; Allgöwer, F.; Findeisen, R.; Fischer, J.; Groß, D.; Hanebeck, U. D.; Kern, B.; Müller, M. A.; Pannek, J.; Reble, M.; Stursberg, O.; Varutti, P. & Worthmann, K. (2014): Distributed and networked model-predictive control,  In Lunze, J. (Ed.): Control Theory of Digitally Networked Systems, Springer Verlag, pp. 111-167.
DOI: 10.1007/978-3-319-01131-8_4

Müller, M. A. & Allgöwer, F. (2014): Distributed MPC for consensus and synchronization,  In Maestre, J. M. & Negenborn, R. (Ed.): Distributed MPC Made Easy, Springer Verlag, pp. 89-100.
DOI: 10.1007/978-94-007-7006-5_5

Faulwasser, T.; Müller, M. A. & Worthmann, K. (Eds.) (2021): Recent Advances in Model Predictive Control - Theory, Algorithms, and Applications,  Springer
ISBN: 978-3-030-63281-6

Müller, M. A. (2014): Distributed and economic model predictive control: beyond setpoint stabilization,  Ph.D. Thesis, Institute for Systems Theory and Automatic Control, University of Stuttgart.

Alsalti, M.; Berberich, J.;Lopez, V. G.; Allgöwer, F. & Müller, M. A. (2022): Practical exponential stability of a robust data-driven nonlinear predictive control scheme,  Supplementary material - technical report
arXiv: 2204.01150

Kleyman, V.; Schaller, M.; Wilson, M.; Mordmüller, M.; Brinkmann, R.; Worthmann, K. & Müller, M. A. (2021): Towards model-based temperature-control for retinal laser therapies,  Proc. Workshop Automed 2021

Kleyman, V.; Abbas, H. S.; Brinkmann, R.; Worthmann, K. & Müller, M. A. (2020): Modelling of Heat Diffusion for Temperature Controlled Retinal Photocoagulation,  Proc. Workshop Automed More info