Adaptive Neural Event-Triggered Optimal Tracking Control for Discrete-Time Pure-Feedback Systems

In this paper, a novel event-triggered (ET) optimal tracking control scheme is proposed for a class of discrete-time pure-feedback nonlinear systems. The variable substitution technique is employed to solve the noncausal problem during the backstepping procedure, which avoids the n-step time delays. The implicit function theorem is adopted to solve the nonaffine terms. To obtain the optimal control performance, the optimal controller is designed based on the adaptive critic design framework. Furthermore, the ET mechanism is embedded between the sensor and the controller. A novel ET condition is designed to seek the balance between network resources and control performance. The proposed ET optimal control scheme guarantees all the closed-loop system signals are uniformly ultimately bounded. Numerical simulation results are presented to verify the effectiveness of the proposed scheme.