Optimal sampling control of nonlinear systems based on adaptive dynamic programming

Abstract

In this paper, a sampling control method based on adaptive dynamic programming is proposed. The general form and cost function of nonlinear systems are given, the famous Hamilton-Jacobi-Bellman (HJB) equation is derived, and the sampling controller is designed via the optimal control input. The neural network control is used to approximate the optimal cost function, and it is proved that the closed-loop system is uniformly ultimately bounded. Finally, numerical simulation is presented to show the feasibility of the proposed method.