Adaptive neural event-triggered control for nonlinear uncertain system with input constraint based on auxiliary system

This article is concerned with the issue of event-triggered based adaptive tracking control for a class of nonlinear uncertain system with input hysteresis. The radial basis function of neural networks (RBFNNs) is utilized to compensate the uncertain parts, where approximation errors are combined into the approximation system. Then, consider such method may extend the developed the weight vector of RBFNNs' dimension, such that computing burdens are increased while the considered system is subjected to network resource constraint. Thus, an adaptive neural event-triggered scheme is designed. Furthermore, aiming to compensate the hysteresis effect, an auxiliary system is incorporated into the control design process. In virtue of backstepping technique, an adaptive neural event-triggered control approach is determined for the considered system, such that all close-loop system signals boundedness is remaining bounded. Theoretical results are verified through the given simulation cases.