执行机构
控制理论(社会学)
计算机科学
模式(计算机接口)
钥匙(锁)
振动
振动控制
控制(管理)
人工神经网络
控制工程
工程类
人工智能
计算机安全
量子力学
操作系统
物理
作者
Donghao Zhang,Linghuan Kong,Wei He,Xinbo Yu
标识
DOI:10.1109/tcyb.2023.3271314
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI