Adaptive optimal tracking control with novel event‐triggered formulation for a type of nonlinear systems

Abstract

SummaryIn this article, an event‐based intelligent critic algorithm is developed to address the optimal tracking control problem for a type of discrete‐time nonlinear systems. The nonlinear optimal tracking control design is replaced by solving the optimal regulation problem of the error system. Then, the generalized value iteration algorithm is employed to obtain the admissible tracking control law with off‐line learning. Next, a novel triggering condition is designed to reduce the update times of the controller and improve the resource utilization. It is emphasized that this triggering condition is established based on the iteration of the time‐triggered mechanism. Moreover, in order to realize the cost guarantee of the error system, the real cost function is proved to possess a predetermined upper bound. By analysis, it is shown that the error system is asymptotically stable while the tracking error and the sampling signal are uniformly ultimately bounded during the process of training neural networks. Finally, two examples are conducted to demonstrate the effectiveness of the proposed algorithm.