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

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Original languageEnglish
Number of pages8
JournalIEEE Transactions on Automatic Control
Publication statusE-pub ahead of print - 30 May 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.

Keywords

    Costs, distributed control, Formation control, Multi-agent systems, multi-agent systems, Optimization, Predictive control, Task analysis, Vehicle dynamics

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Distributed MPC for Self-Organized Cooperation of Multi-Agent Systems. / Kohler, Matthias; Muller, Matthias A.; Allgower, Frank.
In: IEEE Transactions on Automatic Control, 30.05.2024.

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Kohler M, Muller MA, Allgower F. Distributed MPC for Self-Organized Cooperation of Multi-Agent Systems. IEEE Transactions on Automatic Control. 2024 May 30. Epub 2024 May 30. doi: 10.48550/arXiv.2210.10128, 10.1109/TAC.2024.3407633
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