Distributed Model Predictive Control for Periodic Cooperation of Multi-Agent Systems

TL;DR

This paper introduces a distributed model predictive control scheme enabling heterogeneous multi-agent systems to achieve periodic cooperative goals through sequential optimization and minimal communication, ensuring asymptotic convergence.

Contribution

It proposes a novel sequential distributed MPC approach for multi-agent systems with nonlinear dynamics and constraints, focusing on periodic cooperation with minimal communication.

Findings

Agents can asymptotically reach the cooperative periodic goal

The scheme requires only one communication per time step

Simulation demonstrates effective cooperative control

Abstract

We consider multi-agent systems with heterogeneous, nonlinear agents subject to individual constraints that want to achieve a periodic, dynamic cooperative control goal which can be characterised by a set and a suitable cost. We propose a sequential distributed model predictive control (MPC) scheme in which agents sequentially solve an individual optimisation problem to track an artificial periodic output trajectory. The optimisation problems are coupled through these artificial periodic output trajectories, which are communicated and penalised using the cost that characterises the cooperative goal. The agents communicate only their artificial trajectories and only once per time step. We show that under suitable assumptions, the agents can incrementally move their artificial output trajectories towards the cooperative goal, and, hence, their closed-loop output trajectories asymptotically achieve it. We illustrate the scheme with a simulation example.