Details
| Original language | English |
|---|---|
| Pages (from-to) | 3158-3163 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 56 |
| Issue number | 2 |
| Early online date | 22 Nov 2023 |
| Publication status | Published - 2023 |
| Event | 22nd IFAC World Congress - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023 |
Abstract
We consider multi-agent systems with heterogeneous, nonlinear agents subject to individual constraints that want to achieve a periodic, dynamic cooperative control goal which can be characterised by a set and a suitable cost. We propose a sequential distributed model predictive control (MPC) scheme in which agents sequentially solve an individual optimisation problem to track an artificial periodic output trajectory. The optimisation problems are coupled through these artificial periodic output trajectories, which are communicated and penalised using the cost that characterises the cooperative goal. The agents communicate only their artificial trajectories and only once per time step. We show that under suitable assumptions, the agents can incrementally move their artificial output trajectories towards the cooperative goal, and, hence, their closed-loop output trajectories asymptotically achieve it. We illustrate the scheme with a simulation example.
Keywords
- cooperative control, distributed MPC, multi-agent systems, nonlinear systems, Predictive control
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: IFAC-PapersOnLine, Vol. 56, No. 2, 2023, p. 3158-3163.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Distributed Model Predictive Control for Periodic Cooperation of Multi-Agent Systems
AU - Köhler, Matthias
AU - Müller, Matthias A.
AU - Allgöwer, Frank
N1 - Funding Information: F. Allgöwer and M. A. Müller are thankful that this work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - AL 316/11-2-244600449. F. Allgöwer is thankful that this work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075-390740016.
PY - 2023
Y1 - 2023
N2 - We consider multi-agent systems with heterogeneous, nonlinear agents subject to individual constraints that want to achieve a periodic, dynamic cooperative control goal which can be characterised by a set and a suitable cost. We propose a sequential distributed model predictive control (MPC) scheme in which agents sequentially solve an individual optimisation problem to track an artificial periodic output trajectory. The optimisation problems are coupled through these artificial periodic output trajectories, which are communicated and penalised using the cost that characterises the cooperative goal. The agents communicate only their artificial trajectories and only once per time step. We show that under suitable assumptions, the agents can incrementally move their artificial output trajectories towards the cooperative goal, and, hence, their closed-loop output trajectories asymptotically achieve it. We illustrate the scheme with a simulation example.
AB - We consider multi-agent systems with heterogeneous, nonlinear agents subject to individual constraints that want to achieve a periodic, dynamic cooperative control goal which can be characterised by a set and a suitable cost. We propose a sequential distributed model predictive control (MPC) scheme in which agents sequentially solve an individual optimisation problem to track an artificial periodic output trajectory. The optimisation problems are coupled through these artificial periodic output trajectories, which are communicated and penalised using the cost that characterises the cooperative goal. The agents communicate only their artificial trajectories and only once per time step. We show that under suitable assumptions, the agents can incrementally move their artificial output trajectories towards the cooperative goal, and, hence, their closed-loop output trajectories asymptotically achieve it. We illustrate the scheme with a simulation example.
KW - cooperative control
KW - distributed MPC
KW - multi-agent systems
KW - nonlinear systems
KW - Predictive control
UR - http://www.scopus.com/inward/record.url?scp=85184963607&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2304.03002
DO - 10.48550/arXiv.2304.03002
M3 - Conference article
AN - SCOPUS:85184963607
VL - 56
SP - 3158
EP - 3163
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
IS - 2
T2 - 22nd IFAC World Congress
Y2 - 9 July 2023 through 14 July 2023
ER -