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Robust fault reconstruction via learning observers in linear parameter-varying systems subject to loss of actuator effectiveness

机译:线性参数变化系统中通过学习观测器进行的鲁棒故障重构会导致执行器效率下降

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摘要

In this study, the problem of robust fault reconstruction in polytopic linear parameter-varying systems, subject to loss of actuator effectiveness and external disturbances, is investigated using a learning observer (LO). A polytopic LO is proposed to achieve system state estimation and actuator fault reconstruction at the same time. The proposed LO is sensitive to incipient faults because no chattering is induced. Both constant and time-varying faults can be accurately reconstructed and detected. Moreover, simultaneous fault reconstruction and detection can be performed using a single LO because the reconstructed fault signal can be employed as a fault alarm. The stability and convergence of polytopic LO and the uniformly ultimately boundedness of the dynamic error system is proved using Lyapunov stability theory and H techniques. A systematic design of the LO is effectively solved using standard linear-matrix-inequality techniques. At last, simulation results on a satellite system for attitude control verify the effectiveness of the proposed fault-reconstruction approach.
机译:在这项研究中,使用学习观察器(LO)研究了多变量线性参数可变系统中鲁棒故障重建的问题,该问题受执行器有效性和外部干扰的影响。为了实现系统状态估计和执行器故障重构,提出了一种多任务LO。建议的本振对初期故障很敏感,因为不会引起颤动。恒定故障和时变故障都可以被准确地重建和检测。此外,因为可以将重构的故障信号用作故障警报,所以可以使用单个LO执行同时的故障重构和检测。利用Lyapunov稳定性理论和H 技术证明了多变量LO的稳定性和收敛性以及动态误差系统的一致最终有界性。使用标准线性矩阵不等式技术可以有效解决LO的系统设计。最后,在用于姿态控制的卫星系统上的仿真结果验证了所提出的故障重建方法的有效性。

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