Event-Triggered Cooperative Tracking Control of Multiagent Systems With a Dynamic Leader via Approximate Dynamic Programming
Hao Fu, Haodong He, Yang Chen · IEEE Transactions on Artificial Intelligence · 2023
This paper investigates the optimal cooperative tracking control problem for unknown nonlinear multi-agent systems with a real dynamic leader and uncertainties. Its main difficulty lies in eliminating effect of the unmatched uncertainty and noncooperation in the event-triggered manner. To overcome this difficulty, a distributed robust event-triggered control algorithm is developed with the aid of a disturbance observer and robust approximate dynamic programming (ADP). Specifically, this problem is converted into a cooperative multi-player game one for the distributed auxiliary systems employing observer based feed forward compensation, which contributes to reducing conservatism; via an adaptive event-triggered mechanism and a new weight tuning law, a distributed robust ADP approach is proposed with hope to obviate judging the existence of the saddle point beforehand. Moreover, the local neighbor tracking error and the weightest imation error are demonstrated to be uniformly ultimately bounded by means of Lyapunov stability theory. We also prove that Zeno behavior is excluded. A comparative simulation example for multiquadrotor systems is carried out to verify effectiveness and superiority of our algorithm.