Model-free adaptive optimal control for nonlinear multiplayer games with input disturbances

Abstract

In this paper, we investigate a model-free identifier-critic-based optimal adaptive controller for multiplayer games with the input disturbances. Specifically, we first adopt the identifier neural network to identify the system dynamics. Simultaneously, we use the critic neural network to estimate the optimal cost function thereby obtaining the estimated optimal controller. Further taking the input disturbances into consideration, we add a feedback gain into the estimated optimal controller so as to obtain the controller. The learning of the identifier and critic network is online and simultaneous. Then, we analyze the stability of the proposed approach. Eventually, the simulation results illustrate the validity of the proposed controller.