Sensor Fault Detection and Diagnosis for an Unmanned Quadrotor Helicopter

被引:32
|
作者
Zhong, Yujiang [1 ]
Zhang, Wei [1 ]
Zhang, Youmin [2 ]
Zuo, Junyi [1 ]
Zhan, Hao [1 ]
机构
[1] Northwestern Polytech Univ, Sch Aeronaut, Xian, Shaanxi, Peoples R China
[2] Concordia Univ, Dept Mech, Ind, Aerosp Engn, Montreal, PQ, Canada
基金
中国国家自然科学基金; 加拿大自然科学与工程研究理事会;
关键词
Unmanned quadrotor helicopter; Fault diagnosis; IMU sensor fault; Adaptive two-stage extended Kalman filter; OBSERVER-BASED SENSOR; KALMAN FILTER;
D O I
10.1007/s10846-019-01002-4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper proposes a new nonlinear fault detection and diagnosis (FDD) scheme for the inertial measurement unit (IMU) sensor of an unmanned quadrotor helicopter (UQH). To mitigate the impact of model uncertainties, the kinematic model of an UQH rather than the dynamic model is employed to design the FDD scheme. A two-stage extended Kalman filter (TSEKF) is developed for detecting, isolating and identifying IMU sensor faults. Considering that the TSEKF is insensitive to time-varying faults, two adaptive two-stage extended Kalman filters are further proposed by integrating TSEKF with different forgetting factor schemes. Several experiments have been designed and implemented on an UQH platform to test the proposed FDD scheme, where bias fault, drift fault and oscillatory fault are considered. The results demonstrate that the proposed FDD methods are effective for detecting and estimating the IMU sensor faults in different fault scenarios.
引用
收藏
页码:555 / 572
页数:18
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