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An Eigenstructure Assignment Embedded Unknown Input Observe Approach for Actuator Fault Detection in Quadrotor Dynamics

机译：四转子动力学中执行器故障检测的本征结构分配嵌入式未知输入观测方法

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Application of Unmanned Areal Vehicles for both civilian and military demands improved safety conditions to avoid potential malfunction and accidents in critical mission deployment. This paper presents a method for fault detection and identification (FDI) of actuator fault of a quadrotor. A combination of an Unknown Input Observer (UIO) and Beard Basic Fault Detection Filters (BFDF) are used to generate robust and directional residual using unknown input and eignestructure assignment respectively for fault identification and isolation. The uni-directional behavior of the residual will be exploited to isolate the source of the fault by comparing with known or predefined fault directions. The actuator faults are modeled as a loss of effectiveness, Lock-In-Place, Float and Hard Over Failure. The FDI system is used to detect and isolate the actuator faults in quadrotor actuator ( motors). A numerical simulation is done to demonstrate the effectiveness of the UIO and BFDF based FDI algorithm on a model quadrotor.
机译：将无人机用于民用和军用要求改善安全条件，以避免在关键任务部署中潜在的故障和事故。本文提出了一种四旋翼执行器故障的故障检测与识别方法。未知输入观测器（UIO）和胡须基本故障检测过滤器（BFDF）的组合用于分别使用未知输入和特征结构分配来生成鲁棒性和方向性残差，分别用于故障识别和隔离。通过与已知的或预定义的故障方向进行比较，将利用残差的单向行为来隔离故障源。执行器故障建模为有效性损失，就地锁定，浮动和硬故障。 FDI系统用于检测和隔离四旋翼执行器（电动机）中的执行器故障。进行了数值模拟，以证明基于UIO和BFDF的FDI算法在模型四旋翼上的有效性。

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