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.

中文翻译：

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基于辅助系统的输入约束非线性不确定系统自适应神经事件触发控制

本文研究一类具有输入滞后的非线性不确定系统的基于事件触发的自适应跟踪控制问题。神经网络（RBFNN）的径向基函数用于补偿不确定部分，其中逼近误差被组合到逼近系统中。然后，考虑这种方法可以扩展RBFNNs维度的开发权向量，从而在所考虑的系统受到网络资源约束的同时增加计算负担。因此，设计了一种自适应神经事件触发方案。此外，为了补偿滞后效应，在控制设计过程中加入了一个辅助系统。凭借反推技术，为所考虑的系统确定了自适应神经事件触发控制方法，使得所有闭环系统信号有界保持有界。通过给定的仿真案例验证了理论结果。