Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 365-370 |
| Seitenumfang | 6 |
| Fachzeitschrift | IFAC-PapersOnLine |
| Jahrgang | 55 |
| Ausgabenummer | 30 |
| Frühes Online-Datum | 23 Nov. 2022 |
| Publikationsstatus | Veröffentlicht - 2022 |
| Veranstaltung | 25th IFAC Symposium on Mathematical Theory of Networks and Systems, MTNS 2022 - Bayreuth, Deutschland Dauer: 12 Sept. 2022 → 16 Sept. 2022 |
Abstract
In this paper, we present a data-driven distributed model predictive control (MPC) scheme to stabilise the origin of dynamically coupled discrete-time linear systems subject to decoupled input constraints. The local optimisation problems solved by the subsystems rely on a distributed adaptation of the Fundamental Lemma by Willems et al., allowing to parametrise system trajectories using only measured input-output data without explicit model knowledge. For the local predictions, the subsystems rely on communicated assumed trajectories of neighbours. Each subsystem guarantees a small deviation from these trajectories via a consistency constraint. We provide a theoretical analysis of the resulting non-iterative distributed MPC scheme, including proofs of recursive feasibility and (practical) stability. Finally, the approach is successfully applied to a numerical example.
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in: IFAC-PapersOnLine, Jahrgang 55, Nr. 30, 2022, S. 365-370.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Data-driven distributed MPC of dynamically coupled linear systems
AU - Kohler, Matthias
AU - Berberich, Julian
AU - Müller, Matthias A.
AU - Allgower, Frank
PY - 2022
Y1 - 2022
N2 - In this paper, we present a data-driven distributed model predictive control (MPC) scheme to stabilise the origin of dynamically coupled discrete-time linear systems subject to decoupled input constraints. The local optimisation problems solved by the subsystems rely on a distributed adaptation of the Fundamental Lemma by Willems et al., allowing to parametrise system trajectories using only measured input-output data without explicit model knowledge. For the local predictions, the subsystems rely on communicated assumed trajectories of neighbours. Each subsystem guarantees a small deviation from these trajectories via a consistency constraint. We provide a theoretical analysis of the resulting non-iterative distributed MPC scheme, including proofs of recursive feasibility and (practical) stability. Finally, the approach is successfully applied to a numerical example.
AB - In this paper, we present a data-driven distributed model predictive control (MPC) scheme to stabilise the origin of dynamically coupled discrete-time linear systems subject to decoupled input constraints. The local optimisation problems solved by the subsystems rely on a distributed adaptation of the Fundamental Lemma by Willems et al., allowing to parametrise system trajectories using only measured input-output data without explicit model knowledge. For the local predictions, the subsystems rely on communicated assumed trajectories of neighbours. Each subsystem guarantees a small deviation from these trajectories via a consistency constraint. We provide a theoretical analysis of the resulting non-iterative distributed MPC scheme, including proofs of recursive feasibility and (practical) stability. Finally, the approach is successfully applied to a numerical example.
KW - Data-based control
KW - distributed control
KW - large-scale systems
KW - linear systems
KW - predictive control
UR - http://www.scopus.com/inward/record.url?scp=85144821564&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2202.12764
DO - 10.48550/arXiv.2202.12764
M3 - Conference article
AN - SCOPUS:85144821564
VL - 55
SP - 365
EP - 370
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 30
T2 - 25th IFAC Symposium on Mathematical Theory of Networks and Systems, MTNS 2022
Y2 - 12 September 2022 through 16 September 2022
ER -