Data-Based Event-Triggered Cooperative Optimal Output Regulation of Nonlinear Multiagent Systems

This article investigates the data-based cooperative optimal output regulation problem (COORP) for nonlinear strict-feedback multiagent systems (MASs) under an event-triggered mechanism (ETM). By constructing an adaptive distributed observer, each follower can estimate the leader’s dynamic and state. In the control design, a feedforward-feedback control input is proposed based on system data. By utilizing the neural networks (NNs) to learn the solutions of the nonlinear regulator equation and the Hamilton–Jacobi–Bellman (HJB) equation, the feedforward control problem and the optimal feedback control problem can be addressed. Then, an off-policy integral reinforcement learning (IRL)-based optimal cooperative control method is proposed with actor-critic NNs (A-C NNs), and the influence caused by unknown nonlinear dynamics can be handled. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> Through the stability analysis, it is proved that all signals in closed-loop system are uniformly ultimately bounded (UUB), and the system can achieve Nash equilibrium. To demonstrate the effectiveness of the developed optimal control method, a simulation example is provided.