A new self-learning optimal control laws for a class of discrete-time nonlinear systems based on ESN architecture

Abstract

A novel self-learning optimal control method for a class of discrete-time nonlinear systems is proposed based on iteration adaptive dynamic programming (ADP) algorithm. It is proven that the iteration costate functions converge to the optimal one, and a detailed convergence analysis of the iteration ADP algorithm is given. Furthermore, echo state network (ESN) architecture is used as the approximator of the costate function for each iteration. To ensure the reliability of the ESN approximator, the ESN mean square training error is constrained in the satisfactory range. Two simulation examples are given to demonstrate that the proposed control method has a fast response speed due to the special structure and the fast training process.