This paper explores adaptive neural fault-tolerant control for nonlinear systems characterized by a nonstrict-feedback structure, tackling the difficulties arising from unmodeled dynamics and unknown backlash-like hysteresis. A dynamic signal is introduced to mitigate the adverse effects of unmodeled dynamics, while radial basis function neural networks (RBFNNs) are utilized to capture the unknown nonlinear uncertainties presented in the system. Furthermore, the impact of unknown hysteresis input is compensated for by approximating an intermediate variable. By employing the backstepping technique along with neural network approximations, an adaptive neural fault-tolerant control scheme is developed. Through the application of Lyapunov stability theory, the proposed control strategy guarantees the boundedness of all signals within the closed-loop system and ensures that the tracking error meets the specified performance criteria, even in the presence of challenges such as unmodeled dynamics, unknown backlash-like hysteresis, and actuator faults. Two illustrative examples are included to showcase the effectiveness of the proposed control scheme.

中文翻译：

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基于神经网络的自适应容错控制，适用于具有未知反向间隙磁滞和未建模动力学的非线性系统


本文探讨了以非严格反馈结构为特征的非线性系统的自适应神经容错控制，解决了未建模动力学和未知的反冲式磁滞带来的困难。引入动态信号以减轻未建模动力学的不利影响，同时利用径向基函数神经网络 （RBFNN） 来捕获系统中存在的未知非线性不确定性。此外，未知磁滞输入的影响通过近似中间变量来补偿。通过采用反步技术和神经网络近似，开发了一种自适应神经容错控制方案。通过应用 Lyapunov 稳定性理论，所提出的控制策略保证了闭环系统内所有信号的有界性，并确保跟踪误差满足规定的性能标准，即使存在未建模的动力学、未知的反冲式磁滞和执行器故障等挑战。文中包括两个说明性例子，以展示建议的控制方案的有效性。