ADP-based optimal control for a class of nonlinear discrete-time systems with inequality constraints

Abstract

In this paper, the adaptive dynamic programming (ADP) approach is utilized to design a neural-network-based optimal controller for a class of nonlinear discrete-time (DT) systems with inequality constraints. To begin with, the initial constrained optimal control problem is transformed into an infinite horizon optimal control problem by introducing the penalty function. Then, the iterative ADP algorithm is developed to handle the nonlinear optimal control problem with two neural networks. The two neural networks are aimed at generating the optimal cost and the optimal control policy respectively. Finally, the numerical results and analysis are presented to illustrate the performance of the developed method.