Details
| Original language | English |
|---|---|
| Title of host publication | 2024 IEEE 63rd Conference on Decision and Control, CDC 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 6553-6559 |
| Number of pages | 7 |
| ISBN (electronic) | 9798350316339 |
| ISBN (print) | 979-8-3503-1634-6 |
| Publication status | Published - 16 Dec 2024 |
| Event | 63rd IEEE Conference on Decision and Control, CDC 2024 - Milan, Italy Duration: 16 Dec 2024 → 19 Dec 2024 |
Publication series
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| ISSN (Print) | 0743-1546 |
| ISSN (electronic) | 2576-2370 |
Abstract
This article develops a control method for linear time-invariant systems subject to time-varying and a priori unknown cost functions, that satisfies state and input constraints, and is robust to exogenous disturbances. To this end, we combine the online convex optimization framework with a reference governor and a constraint tightening approach. The proposed framework guarantees recursive feasibility and robust constraint satisfaction. Its closed-loop performance is studied in terms of its dynamic regret, which is bounded linearly by the variation of the cost functions and the magnitude of the disturbances. The proposed method is illustrated by a numerical case study of a tracking control problem.
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Mathematics(all)
- Modelling and Simulation
- Mathematics(all)
- Control and Optimization
Cite this
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2024 IEEE 63rd Conference on Decision and Control, CDC 2024. Institute of Electrical and Electronics Engineers Inc., 2024. p. 6553-6559 (Proceedings of the IEEE Conference on Decision and Control).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Robust Control of Constrained Linear Systems using Online Convex Optimization and a Reference Governor
AU - Nonhoff, Marko
AU - Torshan, Mohammad T.Al
AU - Muller, Matthias A.
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024/12/16
Y1 - 2024/12/16
N2 - This article develops a control method for linear time-invariant systems subject to time-varying and a priori unknown cost functions, that satisfies state and input constraints, and is robust to exogenous disturbances. To this end, we combine the online convex optimization framework with a reference governor and a constraint tightening approach. The proposed framework guarantees recursive feasibility and robust constraint satisfaction. Its closed-loop performance is studied in terms of its dynamic regret, which is bounded linearly by the variation of the cost functions and the magnitude of the disturbances. The proposed method is illustrated by a numerical case study of a tracking control problem.
AB - This article develops a control method for linear time-invariant systems subject to time-varying and a priori unknown cost functions, that satisfies state and input constraints, and is robust to exogenous disturbances. To this end, we combine the online convex optimization framework with a reference governor and a constraint tightening approach. The proposed framework guarantees recursive feasibility and robust constraint satisfaction. Its closed-loop performance is studied in terms of its dynamic regret, which is bounded linearly by the variation of the cost functions and the magnitude of the disturbances. The proposed method is illustrated by a numerical case study of a tracking control problem.
UR - http://www.scopus.com/inward/record.url?scp=86000524356&partnerID=8YFLogxK
U2 - 10.1109/CDC56724.2024.10886274
DO - 10.1109/CDC56724.2024.10886274
M3 - Conference contribution
AN - SCOPUS:86000524356
SN - 979-8-3503-1634-6
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 6553
EP - 6559
BT - 2024 IEEE 63rd Conference on Decision and Control, CDC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 63rd IEEE Conference on Decision and Control, CDC 2024
Y2 - 16 December 2024 through 19 December 2024
ER -