Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 7988-7995 |
| Seitenumfang | 8 |
| Fachzeitschrift | IEEE Transactions on Automatic Control |
| Jahrgang | 69 |
| Ausgabenummer | 11 |
| Frühes Online-Datum | 30 Mai 2024 |
| Publikationsstatus | Veröffentlicht - 2024 |
Abstract
We present a sequential distributed model predictive control (MPC) scheme for cooperative control of multi-agent systems with dynamically decoupled heterogeneous nonlinear agents subject to individual constraints. In the scheme, we explore the idea of using tracking MPC with artificial references to let agents coordinate their cooperation without external guidance. Each agent combines a tracking MPC with artificial references, the latter penalized by a suitable coupling cost. They solve an individual optimization problem for this artificial reference and an input that tracks it, only communicating the former to its neighbors in a communication graph. This puts the cooperative problem on a different layer than the handling of the dynamics and constraints, loosening the connection between the two. We provide sufficient conditions on the formulation of the cooperative problem and the coupling cost for the closed-loop system to asymptotically achieve it. Since the dynamics and the cooperative problem are only loosely connected, classical results from distributed optimization can be used to this end. We illustrate the scheme's application to consensus and formation control.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Informatik (insg.)
- Angewandte Informatik
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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in: IEEE Transactions on Automatic Control, Jahrgang 69, Nr. 11, 2024, S. 7988-7995.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Distributed MPC for Self-Organized Cooperation of Multi-Agent Systems
AU - Kohler, Matthias
AU - Muller, Matthias A.
AU - Allgower, Frank
N1 - Publisher Copyright: IEEE
PY - 2024
Y1 - 2024
N2 - We present a sequential distributed model predictive control (MPC) scheme for cooperative control of multi-agent systems with dynamically decoupled heterogeneous nonlinear agents subject to individual constraints. In the scheme, we explore the idea of using tracking MPC with artificial references to let agents coordinate their cooperation without external guidance. Each agent combines a tracking MPC with artificial references, the latter penalized by a suitable coupling cost. They solve an individual optimization problem for this artificial reference and an input that tracks it, only communicating the former to its neighbors in a communication graph. This puts the cooperative problem on a different layer than the handling of the dynamics and constraints, loosening the connection between the two. We provide sufficient conditions on the formulation of the cooperative problem and the coupling cost for the closed-loop system to asymptotically achieve it. Since the dynamics and the cooperative problem are only loosely connected, classical results from distributed optimization can be used to this end. We illustrate the scheme's application to consensus and formation control.
AB - We present a sequential distributed model predictive control (MPC) scheme for cooperative control of multi-agent systems with dynamically decoupled heterogeneous nonlinear agents subject to individual constraints. In the scheme, we explore the idea of using tracking MPC with artificial references to let agents coordinate their cooperation without external guidance. Each agent combines a tracking MPC with artificial references, the latter penalized by a suitable coupling cost. They solve an individual optimization problem for this artificial reference and an input that tracks it, only communicating the former to its neighbors in a communication graph. This puts the cooperative problem on a different layer than the handling of the dynamics and constraints, loosening the connection between the two. We provide sufficient conditions on the formulation of the cooperative problem and the coupling cost for the closed-loop system to asymptotically achieve it. Since the dynamics and the cooperative problem are only loosely connected, classical results from distributed optimization can be used to this end. We illustrate the scheme's application to consensus and formation control.
KW - Costs
KW - distributed control
KW - Formation control
KW - Multi-agent systems
KW - multi-agent systems
KW - Optimization
KW - Predictive control
KW - Task analysis
KW - Vehicle dynamics
KW - Distributed control
KW - predictive control
KW - multiagent systems
UR - http://www.scopus.com/inward/record.url?scp=85194894233&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2210.10128
DO - 10.48550/arXiv.2210.10128
M3 - Article
AN - SCOPUS:85194894233
VL - 69
SP - 7988
EP - 7995
JO - IEEE Transactions on Automatic Control
JF - IEEE Transactions on Automatic Control
SN - 0018-9286
IS - 11
ER -