Self-learning control schemes for two-person zero-sum differential games of continuous-time nonlinear systems with saturating controllers

Abstract

In this paper, an adaptive dynamic programming (ADP)-based self-learning algorithm is developed for solving the two-person zero-sum differential games for continuous-time nonlinear systems with saturating controllers. Optimal control pair is iteratively obtained by the proposed ADP algorithm that makes the performance index function reach the saddle point of the zero-sum differential games. It shows that the iterative control pairs stabilize the nonlinear systems and the iterative performance index functions converge to the saddle point. Finally, a simulation example is given to illustrate the performance of the proposed method. © 2012 Springer-Verlag.