The adaptive control for strict-feedback nonlinear systems has drawn a lot of attention in various communities. Since neural network is a useful universal-approximator to approximate unknown plant model, the neural network–based adaptive control for nonlinear systems has attracted substantial interest over decades. Furthermore, to reduce the controller updating and save the control resource, the event-triggered mechanism has been widely applied. In this paper, the RBF neural network is applied to construct the state and composite disturbance observers and the back-stepping and Lyapunov-like method are applied to design the event-triggered adaptive controller. The theoretical framework of adaptive fault-tolerant control issue for strict-feedback nonlinear system that suffer from both unknown mismatched disturbance and actuator failures is formulated. This paper comes up with a novel event-triggered control strategy to guarantee that the tracking issue is resolved with better desired performance. In this study, a unified theoretical mechanism is developed to tackle the case where some factors consisting of unknown state variables, unknown mismatched disturbance, and actuator failures as well as event-triggered effects are merged together. We expect to extend the proposed method for the self-triggered case.

严格反馈非线性系统的自适应控制引起了各界的广泛关注。由于神经网络是逼近未知植物模型的有用的通用近似器，因此几十年来，基于神经网络的非线性系统自适应控制引起了人们的极大兴趣。此外，为了减少控制器的更新并节省控制资源，事件触发机制已被广泛应用。本文采用RBF神经网络构造状态和复合干扰观测器，并采用反步法和类Lyapunov方法设计事件触发的自适应控制器。提出了具有未知失配扰动和执行器故障的严格反馈非线性系统的自适应容错控制问题的理论框架。本文提出了一种新颖的事件触发控制策略，以确保以更好的期望性能解决跟踪问题。在这项研究中，开发了一个统一的理论机制来解决某些情况，其中包括一些未知状态变量，未知失配干扰，执行器故障以及事件触发效应。我们希望将所提出的方法扩展到自触发情况。开发了一种统一的理论机制来解决将未知状态变量，未知失配干扰，执行器故障以及事件触发效应等因素合并在一起的情况。我们希望将所提出的方法扩展到自触发情况。开发了一种统一的理论机制来解决将未知状态变量，未知失配干扰，执行器故障以及事件触发效应等因素合并在一起的情况。我们希望将所提出的方法扩展到自触发情况。