This article concentrates on the event-based collaborative design for strict-feedback systems with uncertain nonlinearities. The controller is designed based on neural network (NN) weights adaptive law. The controller and NN weights adaptive law are only updated at the triggering instants determined by a novel composite triggering threshold. Considering the conservativeness of event condition, the state-model error is integrated into constructing the composite condition and NN weights adaptive law. In the context of the proposed mechanism, the requirements of system information and the allowable range of event-triggering error are relaxed. The number of triggering instants is greatly reduced without deteriorating the system performance. Moreover, the stability of the closed-loop is proved by the Lyapunov method following time-interval and sampling instants. Simulation results show the effectiveness of the scheme proposed in this article.

中文翻译：

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复合条件下不确定非线性系统的事件触发自适应控制

本文重点研究具有不确定非线性的严格反馈系统的基于事件的协同设计。该控制器是基于神经网络（NN）权重自适应律设计的。控制器和 NN 权重自适应律仅在由新的复合触发阈值确定的触发时刻更新。考虑到事件条件的保守性，在构建复合条件和NN权重自适应律时将状态模型误差纳入其中。在所提出机制的背景下，放宽了对系统信息的要求和事件触发错误的允许范围。在不降低系统性能的情况下，触发时刻的数量大大减少。而且，闭环的稳定性通过时间间隔和采样时刻的李雅普诺夫方法证明。仿真结果表明了本文提出的方案的有效性。