In many practical control applications, the mathematical model of the system to be controlled or the environment of the system is unknown or uncertain, making the use of most standard control designs impossible. Data-based control is the area of research that investigates the development of stabilizing controllers using only suitable collected input-output information, without any knowledge of the system model. Similarly, learning-based control uses artificial intelligence or online learning algorithms to learn the model or the environment of the system or directly determine a stabilizing controller from input-output data. These control strategies have strong potential applications for control of nonlinear systems, where obtaining accurate system models is often difficult or practically unfeasible.
In particular, our current research focuses on
- online convex optimization for control of dynamical systems,
- data-based descriptions of nonlinear systems,
- the development of data-driven predictive controllers,
- the design of off-policy reinforcement learning algorithms to obtain state-feedback controllers, and
- the study of data-based controllers for irregularly sampled systems.
SELECTED PUBLICATIONS
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(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 -
(2021): Data-driven online convex optimization for control of dynamical systems, Accepted to IEEE Conference on Decision and Control 2021
arXiv: 2103.09127 -
(2020): Data-Driven Model Predictive Control with Stability and Robustness Guarantees, IEEE Transactions on Automatic Control
DOI: 10.1109/TAC.2020.3000182
SELECTED PROJECTS
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Cont4Med - Estimation and control under limited information with application to biomedical systemsLed by: Prof. Dr.-Ing. Matthias MüllerTeam:Year: 2021Funding: This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 948679).Duration: 2021 - 2025