Filter-Based Fully Distributed Output Regulation of Heterogeneous Learning Agents

This paper proposes a novel filter-based model-free reinforcement learning (RL) event-triggered control (ETC) method for the fully distributed robust leaderless cooperative output regulation (COR) of unknown heterogeneous multi-agent systems (MASs) with external disturbances over directed graphs. First, the fully distributed event-triggered observers are designed to generate an autonomous system for the robust leaderless COR, in which the frequency of signal transmission and computational burden are significantly reduced, and the Zeno behavior is strictly ruled out. Then, a filter-based model-free RL algorithm without integration operation is developed to obtain the solution of the internal model-based augmented algebraic Riccati equation (AARE) and to release the requirement of recording complete and continuous data. Moreover, with some adaptive parameters, the robust leaderless COR is solved in a fully distributed manner without involving any global information of directed MASs. Finally, simulation results on RLC circuits are illustrated to show the feasibility and effectiveness of the proposed control scheme.