Adaptive Neural Control of Pure-Feedback Nonlinear Systems With Event-Triggered Communications

This paper is concerned with the adaptive event-triggered control problem for a class of pure-feedback nonlinear systems. Unlike the existing results where the control execution is periodic, the new proposed scheme updates the controller and the neural network weights only when desired control specifications cannot be guaranteed. Clearly, this can largely reduce the amount of transmission data. Besides, since the event-trigger error is discontinuous because of the event-triggering mechanism, the stability analysis in the classical sense may not be guaranteed. To solve this problem, we formulate the event-triggered network control systems as a nonlinear impulsive dynamical system, and a novel Lyapunov theorem is used to show the stability properties of the closed-loop systems. Finally, two simulation examples are given to illustrate the effectiveness of the theoretical results.