Distributed MPC for Self-Organized Cooperation of Multi-Agent Systems

verfasst von: Matthias Kohler, Matthias A. Muller, Frank Allgower
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.
Organisationseinheit(en): Institut für Regelungstechnik
Externe Organisation(en): Universität Stuttgart
Typ: Artikel
Journal: IEEE Transactions on Automatic Control
Band: 69
Seiten: 7988-7995
Anzahl der Seiten: 8
ISSN: 0018-9286
Publikationsdatum: 2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Steuerungs- und Systemtechnik, Angewandte Informatik, Elektrotechnik und Elektronik
Elektronische Version(en): https://doi.org/10.48550/arXiv.2210.10128 (Zugang: Offen)
https://doi.org/10.1109/TAC.2024.3407633 (Zugang: Geschlossen)

BibTeX

@article{15df3c75cbb1457a9c1ad45010bcfa13,
title = "Distributed MPC for Self-Organized Cooperation of Multi-Agent Systems",
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.",
keywords = "Costs, distributed control, Formation control, Multi-agent systems, multi-agent systems, Optimization, Predictive control, Task analysis, Vehicle dynamics, Distributed control, predictive control, multiagent systems",
author = "Matthias Kohler and Muller, {Matthias A.} and Frank Allgower",
note = "Publisher Copyright: IEEE",
year = "2024",
doi = "10.48550/arXiv.2210.10128",
language = "English",
volume = "69",
pages = "7988--7995",
journal = "IEEE Transactions on Automatic Control",
issn = "0018-9286",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "11",
}