Fully Distributed Event-Triggered Control for Containment of Fully Heterogeneous MASs Using Filter-Based RL

The complexity of multi-agent systems (MASs) poses challenges in containment control, especially with fully heterogeneous agents influenced by multiple leaders. Traditional methods rely on continuous communication, global information, or homogeneous dynamics, limiting real-world applicability. In this paper, a model-free reinforcement learning (RL) based fully distributed event-triggered control (ETC) framework is proposed. First, an integrated ETC framework is designed for directed graphs, allowing agents to observe multiple leaders simultaneously. To further reduce the communication burden, an improved fully distributed ETM with $\sigma$-modification is introduced, eliminating global information dependency and enhancing communication efficiency. Based on the observed information, for unknown heterogeneous dynamics, a filter-based model-free RL algorithm solves a fully distributed normalized level of influence (NLI) based augmented algebraic Riccati equation (AARE), enabling online learning from input-output data without system modeling while reducing computational complexity. Simulations validate the framework's effectiveness, demonstrating improved control scalability, communication efficiency, and adaptability due to its fully model-free nature.