Learning-Based Distributed Robust Formation Control Framework for Heterogeneous Multi-Agent Systems under Disturbances

Abstract

This paper investigates a distributed time-varying output formation control framework for heterogeneous multi-agent systems (MASs) based on reinforcement learning (RL) method with multiple leaders and various disturbances. The outputs of followers are designed to accomplish robust tracking along with the movement of leaders' convex combination while achieving a predefined configuration of time-varying formation. A three-layer framework, composed of a distributed adaptive finite-time observer, an off-policy RL-based optimal tracking controller and a robust formation controller, is proposed in a model-free manner while neither the global information of graph nor followers' dynamics is utilized. The stability of this integrated observer-based controller is analyzed using Lyapunov theory which indicates that the formation tracking error asymptotically converges to zero under both external and internal unknown disturbances. Simulation results provide a detailed validation of the proposed control architecture.