This article proposes an adaptive fault-tolerant control (AFTC) approach based on a fixed-time sliding mode for suppressing vibrations of an uncertain, stand-alone tall building-like structure (STABLS). The method incorporates adaptive improved radial basis function neural networks (RBFNNs) within the broad learning system (BLS) to estimate model uncertainty and uses an adaptive fixed-time sliding mode approach to mitigate the impact of actuator effectiveness failures. The key contribution of this article is its demonstration of theoretically and practically guaranteed fixed-time performance of the flexible structure against uncertainty and actuator effectiveness failures. Additionally, the method estimates the lower bound of actuator health when it is unknown. Simulation and experimental results confirm the efficacy of the proposed vibration suppression method.

中文翻译：

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具有执行器故障的灵活智能结构的固定时间控制：广泛的学习系统方法


本文提出了一种基于固定时间滑模的自适应容错控制（AFTC）方法，用于抑制不确定的独立高层建筑结构（STABLS）的振动。该方法将自适应改进的径向基函数神经网络 (RBFNN) 纳入广泛学习系统 (BLS) 中，以估计模型不确定性，并使用自适应固定时间滑动模式方法来减轻执行器有效性故障的影响。本文的主要贡献是从理论上和实践上证明了柔性结构针对不确定性和执行器有效性故障的固定时间性能。此外，该方法还可以在未知时估计执行器健康状况的下限。仿真和实验结果证实了所提出的振动抑制方法的有效性。