Event-Triggered Fixed-Time Optimal Consensus for Multi-Agent Systems

This paper develops a framework for solving the fixed-time optimal control problem of multi-agent systems (MASs) under dynamic event-triggered mechanism (DETM). To find the optimal control law, a critic-only adaptive dynamic programming (ADP) online learning algorithm is proposed to obtain the solution of Hamilton-Jacobi-Bellman (HJB) equation within a fixed time. By the designed experience replay learning rule employing sufficiently rich recorded and current data, the critic weights are updated to approximate the value function and its gradient. The unmanned aerial vehicle (UAV) systems are adopted to verify the feasibility of the presented approach.