Sensor Fault Detection and Diagnosis for an Unmanned Quadrotor Helicopter
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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.
KeywordsUnmanned quadrotor helicopter Fault diagnosis IMU sensor fault Adaptive two-stage extended Kalman filter
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The authors would like to thank the financial support received from the National Natural Science Foundation of China (No. 61473227, 11472222, 61573282 and 61833013) and Natural Sciences and Engineering Research Council of Canada. Authors would also like to thank the editor and the reviewers for the valuable comments and suggestions to improve the quality of the paper through review process.
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