Approximate Optimal tracking Control for Nonlinear Discrete-time Switched Systems via Approximate Dynamic Programming

In this paper, an iterative approximate dynamic programming algorithm is proposed for solving the approximate optimal tracking control problem for a class of nonlinear discrete-time switched systems. Firstly, the optimal tracking problem is converted into designing an optimal regulator for the tracking error dynamics. And then, the iterative approximate dynamic programming algorithm is proposed to obtain the approximate solution of the Hamilton-Jacobi-Bellman (HJB) equation. Next, two neural networks are used to approximate the iterative cost function and the iterative control law, respectively. Finally, simulation results are given to verify the effectiveness of the proposed algorithm.