Optimal Tracking Control for Nonlinear Game Systems Using Dynamic Event-Triggering Strategy

This paper investigates the optimal tracking control issue of nonlinear nonzero-sum game systems with partially unknown dynamics. An adaptive dynamic programming approach is presented on the basis of a dynamic event-triggering mechanism. This paper first introduces a discount coefficient and augmented vectors into the cost function, transforming the optimal tracking control into an optimal regulation control problem. Subsequently, a single critic neural network is proposed to seek for Nash equilibrium. By defining a dynamic variable, a dynamic event-triggering rule is devised with historical data, which causes the larger event-triggering intervals. Moreover, the uniform ultimate boundness properties of the neural network estimation error and the tracking error have been proved. Finally, a numerical simulation verifies the correctness of the proposed method.