Dynamic Event-Triggered Optimal Control Strategy of Unknown Nonlinear Systems

This paper presents an optimal control method based on dynamic event-triggered control (ETC) for unknown nonlinear systems. Firstly, according to the system, we set up the Hamiltonian-Jacobian-Bellman (HJB) equation to derive the optimal problem. Secondly, by designing a dynamic event-triggered control (ETC) scheme which can ensure the stability of the system to achieve the goal of saving communication and computing resources. Then, an Identifier neural network (NN) is used to approximate the dynamic equation of the system to solve the unknown dynamic problem. The given weight matrix is updated to ensure the convergence of the state error. Next, we design a critic NN to approximate the optimal performance index of the system, and the convergence of the system state and its weight matrix is proved by theoretical analysis. Finally, a simulation example is used to verify the effectiveness of the proposed method.