Model predictive control (MPC) is a highly successful optimisation-based control technique. It involves repeatedly solving a finite-horizon optimal control problem at each sampling time and applying the first part of the corresponding optimal solution to the system up to the next sampling time. The main advantages of MPC, and the reasons for its popularity and widespread success in many application areas, are that
- satisfaction of hard constraints on states and inputs can be guaranteed,
- optimization of a performance criterion can be incorporated directly in the controller design,
- it can be applied to general nonlinear systems with possibly multiple inputs.
Our research focuses on various current topics of interest in MPC. These include developing robust, stochastic and adaptive MPC schemes for uncertain systems; considering more general control objectives than setpoint/trajectory tracking (e.g. economic MPC); studying distributed MPC schemes for large-scale systems; and investigating data- and learning-based MPC methods. We are also investigating the application of MPC to various problems, including reactive test environments for autonomous vehicles, optimal medication strategies for thyroid diseases and multimodal energy systems.
Selected Publications
-
(2024): Disturbance feedback-based model predictive control in uncertain dynamic environments, 8th IFAC Conference on Nonlinear Model Predictive Control (NMPC 2024), IFAC-PapersOnLine. Vol. 58, pp. 146-152
DOI: 10.1016/j.ifacol.2024.09.023
arXiv: 2404.09893 -
(2024): Configuration-Constrained Tube MPC, Automatica, 2024, vol. 163, p. 111543
DOI: 10.1016/j.automatica.2024.111543
arXiv: 2208.12554 -
(2022): Approximate dissipativity of cost-interconnected systems in distributed economic MPC, IEEE Transactions on Automatic Control
DOI: 10.1109/TAC.2022.3173028 -
(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 -
(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 -
(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 -
(2018): Economic Nonlinear Model Predictive Control, Foundations and Trends in Systems and Control,, vol. 5, no. 1, pp. 1-98.
DOI: 10.1561/2600000014 -
(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 -
(2017): Quadratic costs do not always work in MPC, Automatica, vol. 82, pp. 269 - 277.
DOI: 10.1016/j.automatica.2017.04.058 -
(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
Selected Projects
-
Robust and stochastic economic model predictive controlLed by: Prof. Dr.-Ing. Matthias MüllerTeam:Year: 2020Funding: Deutsche Forschungsgemeinschaft (DFG) - 279734922Duration: 2020 - 2024
-
Multi-vehicle trajectory planning using MPCLed by: Prof. Dr.-Ing. Matthias MüllerTeam:Year: 2021Funding: Industrial ProjectDuration: 2021 - 2025