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Fault Reconstruction and Accommodation in Linear Parameter-Varying Systems via Learning Unknown-Input Observers

机译:通过学习未知输入的观测器重构线性参数变化系统中的故障

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This paper addresses the problem of observer-based fault reconstruction and accommodation for polytopic linear parameter-varying (LPV) systems. A polytopic representation of an LPV system subject to actuator faults and external disturbances is first established; then, a novel polytopic learning unknown-input observer (LUIO) is constructed for simultaneous state estimation and robust fault reconstruction. The stability of the presented LUIO is proved using Lyapunov stability theory together with H-infinity techniques. Further, using reconstructed fault information, a reconfigurable fault-tolerant controller is designed to compensate for the influence of actuator faults by stabilizing the closed-loop system. At last, an aircraft example is employed to illustrate the effectiveness and practicability of the proposed techniques.
机译:本文解决了多线性参数可变(LPV)系统基于观测器的故障重建和适应问题。首先建立受执行器故障和外部干扰影响的LPV系统的多面表示;然后,构造了一种新颖的多主题学习未知输入观察器(LUIO),用于同时状态估计和鲁棒故障重建。利用Lyapunov稳定性理论以及H-infinity技术证明了所提出的LUIO的稳定性。此外,使用重构的故障信息,设计了可重构的容错控制器,以通过稳定闭环系统来补偿执行器故障的影响。最后,以飞机为例说明了所提技术的有效性和实用性。

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