TY - JOUR

T1 - Distributed MPC for Self-Organized Cooperation of Multi-Agent Systems

AU - Köhler, Matthias

AU - Müller, Matthias A.

AU - Allgöwer, Frank

N1 - Publisher Copyright: IEEE

PY - 2024/11

Y1 - 2024/11

N2 - 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.

AB - 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.

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 -