RL-Based Adaptive Optimal Bipartite Consensus Control for Nonlinear Heterogeneous MASs via Event-Triggered State Feedback

This article investigates a leader-following bipartite consensus issue for uncertain nonlinear heterogeneous multiagent systems (MASs). Initially, within the framework of optimal control theory, we employ the reinforcement learning (RL) algorithm to derive an approximate solution to the Hamilton-Jacobi-Bellman equation (HJBE). Specifically, the neural networks (NNs) are utilized to construct the Actor-Critic structure with the aim of implementing control behavior and evaluating system performance, respectively. An additional network is employed to address nonlinear uncertainties existing in the system. Furthermore, we design a static threshold event-triggered mechanism (ETM) to achieve the event-triggered state feedback-based control strategy. By utilizing this event-triggered state information, we reconstruct the approximate optimal controller and update laws of neural network weights, effectively reducing the communication burden while ensuring that all signals of the MASs remain bounded. Finally, two simulation examples are carried out to demonstrate the feasibility of the proposed method.