Distributed MPC for Self-Organized Cooperation of Multiagent Systems -- Extended Version

TL;DR

This paper introduces a sequential distributed MPC scheme for multi-agent systems that enables cooperative control through artificial references, allowing agents to coordinate without external guidance while respecting individual constraints.

Contribution

It proposes a novel distributed MPC approach using artificial references and coupling costs, loosening the connection between dynamics and cooperation for improved scalability.

Findings

The scheme achieves asymptotic cooperation under certain conditions.

Application to consensus and formation control demonstrates effectiveness.

Classical distributed optimization results support the theoretical guarantees.

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.