Neural-network-based robust optimal control of uncertain nonlinear systems using model-free policy iteration algorithm

Abstract

In this paper, we establish a robust optimal control law for a class of continuous-time uncertain nonlinear systems by using a neural-network-based model-free policy iteration approach. The robust control law of the original uncertain nonlinear system is derived by adding a feedback gain to the optimal control law of the nominal system. It is proven that this robust control law can achieve optimality under a specified cost function. Then, the neural-network-based model-free policy iteration algorithm is developed to solve the Hamilton-Jacobi-Bellman equation corresponding to the nominal system without system dynamics. The actor-critic technique and the least squares implementation method are used to obtain the optimal control policy of the nominal system. A numerical simulation is given to verify the applicability of the present robust optimal control scheme.