Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 60th IEEE Conference on Decision and Control, CDC 2021 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 3640-3645 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9781665436595 |
| ISBN (Print) | 978-1-6654-3660-1 |
| Publikationsstatus | Veröffentlicht - 2021 |
| Veranstaltung | 60th IEEE Conference on Decision and Control, CDC 2021 - Austin, USA / Vereinigte Staaten Dauer: 14 Dez. 2021 → 17 Dez. 2021 |
Publikationsreihe
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| Band | 2021-December |
| ISSN (Print) | 0743-1546 |
| ISSN (elektronisch) | 2576-2370 |
Abstract
We propose a data-driven online convex optimization algorithm for controlling dynamical systems. In particular, the control scheme makes use of an initially measured input-output trajectory and behavioral systems theory which enable it to handle unknown discrete-time linear time-invariant systems as well as a priori unknown time-varying cost functions. Further, only output feedback instead of full state measurements is required for the proposed approach. Analysis of the closed loop's performance reveals that the algorithm achieves sublinear regret if the variation of the cost functions is sublinear. The effectiveness of the proposed algorithm, even in the case of noisy measurements, is illustrated by a simulation example.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Mathematik (insg.)
- Modellierung und Simulation
- Mathematik (insg.)
- Steuerung und Optimierung
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
60th IEEE Conference on Decision and Control, CDC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. S. 3640-3645 (Proceedings of the IEEE Conference on Decision and Control; Band 2021-December).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Data-driven online convex optimization for control of dynamical systems
AU - Nonhoff, Marko
AU - Muller, Matthias A.
PY - 2021
Y1 - 2021
N2 - We propose a data-driven online convex optimization algorithm for controlling dynamical systems. In particular, the control scheme makes use of an initially measured input-output trajectory and behavioral systems theory which enable it to handle unknown discrete-time linear time-invariant systems as well as a priori unknown time-varying cost functions. Further, only output feedback instead of full state measurements is required for the proposed approach. Analysis of the closed loop's performance reveals that the algorithm achieves sublinear regret if the variation of the cost functions is sublinear. The effectiveness of the proposed algorithm, even in the case of noisy measurements, is illustrated by a simulation example.
AB - We propose a data-driven online convex optimization algorithm for controlling dynamical systems. In particular, the control scheme makes use of an initially measured input-output trajectory and behavioral systems theory which enable it to handle unknown discrete-time linear time-invariant systems as well as a priori unknown time-varying cost functions. Further, only output feedback instead of full state measurements is required for the proposed approach. Analysis of the closed loop's performance reveals that the algorithm achieves sublinear regret if the variation of the cost functions is sublinear. The effectiveness of the proposed algorithm, even in the case of noisy measurements, is illustrated by a simulation example.
UR - http://www.scopus.com/inward/record.url?scp=85126057457&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2103.09127
DO - 10.48550/arXiv.2103.09127
M3 - Conference contribution
AN - SCOPUS:85126057457
SN - 978-1-6654-3660-1
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 3640
EP - 3645
BT - 60th IEEE Conference on Decision and Control, CDC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 60th IEEE Conference on Decision and Control, CDC 2021
Y2 - 14 December 2021 through 17 December 2021
ER -