Event-triggered adaptive neural network controller for uncertain nonlinear system

In this paper, an event-triggered adaptive controller, consisting of a basic adaptive neural network controller and an event-triggered mechanism, is developed for a class of single-input and single-output high-order nonlinear systems with neural network approximation. Both the static and the dynamic event-triggered mechanisms are proposed in our design, without the input-state stability (ISS) assumption which is needed in most existing results. It is shown that the proposed methods can ensure that the closed loop system is globally stable. The minimal inter-event time internal is lower bounded by a positive number so that no Zeno behavior occurs. Finally, the numerical simulations are presented to illustrate our theory.