Sensor Fault Detection and Diagnosis for an Unmanned Quadrotor Helicopter

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 th...

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Bibliographic Details
Published in:Journal of intelligent & robotic systems 2019-12, Vol.96 (3-4), p.555-572
Main Authors: Zhong, Yujiang, Zhang, Wei, Zhang, Youmin, Zuo, Junyi, Zhan, Hao
Format: Article
Language:English
Subjects:
Adaptive filters
Analysis
Artificial Intelligence
Control
Dynamic models
Electrical Engineering
Engineering
Extended Kalman filter
Fault detection
Fault diagnosis
Flying-machines
Inertial platforms
Inertial sensing devices
Measuring instruments
Mechanical Engineering
Mechatronics
Robotics
Sensors
Unmanned helicopters
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Summary: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.
ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-019-01002-4