Details
| Original language | English |
|---|---|
| Title of host publication | 2022 IEEE 61st Conference on Decision and Control, CDC 2022 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1105-1110 |
| Number of pages | 6 |
| ISBN (electronic) | 9781665467612 |
| ISBN (print) | 978-1-6654-6760-5, 978-1-6654-6762-9 |
| Publication status | Published - 2022 |
| Event | 61st IEEE Conference on Decision and Control, CDC 2022 - Cancun, Mexico Duration: 6 Dec 2022 → 9 Dec 2022 |
Publication series
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| Volume | 2022-December |
| ISSN (Print) | 0743-1546 |
| ISSN (electronic) | 2576-2370 |
Abstract
Data-driven model predictive control (DD-MPC) based on Willems' Fundamental Lemma has received much attention in recent years, allowing to control systems directly based on an implicit data-dependent system description. The literature contains many successful practical applications as well as theoretical results on closed-loop stability and robustness. In this paper, we provide a tutorial introduction to DD-MPC for unknown linear time-invariant (LTI) systems with focus on (robust) closed-loop stability. We first address the scenario of noise-free data, for which we present a DD-MPC scheme with terminal equality constraints and derive closed-loop properties. In case of noisy data, we introduce a simple yet powerful approach to analyze robust stability of DD-MPC by combining continuity of DD-MPC w.r.t. noise with inherent robustness of model-based MPC, i.e., robustness of nominal MPC w.r.t. small disturbances. Moreover, we discuss how the presented proof technique allows to show closed-loop stability of a variety of DD-MPC schemes with noisy data, as long as the corresponding model-based MPC is inherently robust.
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Mathematics(all)
- Modelling and Simulation
- Mathematics(all)
- Control and Optimization
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2022 IEEE 61st Conference on Decision and Control, CDC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1105-1110 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2022-December).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Stability in data-driven MPC
T2 - 61st IEEE Conference on Decision and Control, CDC 2022
AU - Berberich, Julian
AU - Kohler, Johannes
AU - Muller, Matthias A.
AU - Allgower, Frank
N1 - Funding Information: F. Allgöwer is thankful that his work was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2075 - 390740016 and under grant 468094890. F. Allgöwer acknowledges the support by the Stuttgart Center for Simulation Science (SimTech). M. A. Müller is thankful that his work was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 948679). J. Berberich thanks the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting him.
PY - 2022
Y1 - 2022
N2 - Data-driven model predictive control (DD-MPC) based on Willems' Fundamental Lemma has received much attention in recent years, allowing to control systems directly based on an implicit data-dependent system description. The literature contains many successful practical applications as well as theoretical results on closed-loop stability and robustness. In this paper, we provide a tutorial introduction to DD-MPC for unknown linear time-invariant (LTI) systems with focus on (robust) closed-loop stability. We first address the scenario of noise-free data, for which we present a DD-MPC scheme with terminal equality constraints and derive closed-loop properties. In case of noisy data, we introduce a simple yet powerful approach to analyze robust stability of DD-MPC by combining continuity of DD-MPC w.r.t. noise with inherent robustness of model-based MPC, i.e., robustness of nominal MPC w.r.t. small disturbances. Moreover, we discuss how the presented proof technique allows to show closed-loop stability of a variety of DD-MPC schemes with noisy data, as long as the corresponding model-based MPC is inherently robust.
AB - Data-driven model predictive control (DD-MPC) based on Willems' Fundamental Lemma has received much attention in recent years, allowing to control systems directly based on an implicit data-dependent system description. The literature contains many successful practical applications as well as theoretical results on closed-loop stability and robustness. In this paper, we provide a tutorial introduction to DD-MPC for unknown linear time-invariant (LTI) systems with focus on (robust) closed-loop stability. We first address the scenario of noise-free data, for which we present a DD-MPC scheme with terminal equality constraints and derive closed-loop properties. In case of noisy data, we introduce a simple yet powerful approach to analyze robust stability of DD-MPC by combining continuity of DD-MPC w.r.t. noise with inherent robustness of model-based MPC, i.e., robustness of nominal MPC w.r.t. small disturbances. Moreover, we discuss how the presented proof technique allows to show closed-loop stability of a variety of DD-MPC schemes with noisy data, as long as the corresponding model-based MPC is inherently robust.
UR - http://www.scopus.com/inward/record.url?scp=85147043272&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2205.11859
DO - 10.48550/arXiv.2205.11859
M3 - Conference contribution
AN - SCOPUS:85147043272
SN - 978-1-6654-6760-5
SN - 978-1-6654-6762-9
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1105
EP - 1110
BT - 2022 IEEE 61st Conference on Decision and Control, CDC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 December 2022 through 9 December 2022
ER -