Fixed-Time Control for a Flexible Smart Structure With Actuator Failure: A Broad Learning System Approach
提出一种基于固定时间滑模的自适应容错控制方法，用于抑制不确定的独立高层建筑类结构的振动，通过宽度学习系统中的径向基神经网络估计模型不确定性，并处理执行器效能故障。
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.