Adaptive-Critic-Based Event-Triggered Intelligent Cooperative Control for a Class of Second-Order Constrained Multiagent Systems

An event-based intelligent cooperative control policy is developed in this article for a class of second-order multiagent systems (MASs). The followers are subject to external disturbances and output constraints, in which the constraint ranges are asymmetric and time-varying. By using one-to-one nonlinear mapping technique to obtain equivalent unconstrained systems and designing a distributed observer to estimate the leader's states, a simplified unconstrained tracking control task is established for each follower. After that, event-driven distributed optimal
$H_\infty$
controllers are designed to mitigate the effects of external disturbances, which are obtained from a set of decoupled Hamilton–Jacobi–Isaacs (HJI) equations, where the proposed event-triggered strategy is dynamic. Aiming at solving the HJI equations more efficiently, an event-based adaptive dynamic programming (ADP) learning algorithm applying only critic neural networks (NNs) is developed. And the critic NN weights are convergent by using stored data under finite excitation condition. Finally, the validity of the developed control scheme is demonstrated by multiple single-link robot arm systems.