Distributed MPC for Self-Organized Cooperation of Multiagent Systems

In this article, we present a sequential distributed model predictive control (MPC) scheme for cooperative control of multiagent systems with dynamically decoupled heterogeneous nonlinear agents subject to individual constraints. In the scheme, we explore the idea of using tracking MPC with artifici...

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Bibliographic Details
Published in:IEEE transactions on automatic control 2024-11, Vol.69 (11), p.7988-7995
Main Authors: Kohler, Matthias, Muller, Matthias A., Allgower, Frank
Format: Article
Language:English
Subjects:
Closed loops
Communication
Constraints
Control systems
Cooperation
Cooperative control
Costs
Distributed control
Feedback control
Formation control
Multi-agent systems
Multiagent systems
Nonlinear control
Nonlinear systems
Optimization
Predictive control
Task analysis
Tracking
Vehicle dynamics
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Summary:In this article, we present a sequential distributed model predictive control (MPC) scheme for cooperative control of multiagent 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.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2024.3407633