Fault detection and isolation for Unmanned Aerial Vehicle sensors by using extended PMI filter. Issue 24 (2018) - Guo, Dingfei; Wang, Yulin; Zhong, Maiying; Zhao, Yan
Abstract: Fault detection and isolation (FDI) plays an important role in guaranteeing system safety and reliability for unmanned aerial vehicles (UAVs). This paper focuses on developing a method for detecting UAV sensor faults by using existing sensors, such as pitot tube, gyro, accelerometer and wind angle sensor. We formulate the kinematics as a nonlinear state space system, which requires no dynamic information and thus is applicable to all aircraft. To illustrate the method, we investigate five fault-detection scenarios, namely, faulty pitot tube, angle-of-attack sensor, sideslip sensor, accelerometer and gyro, and design a FDI structure including five faulty sensors. Then, considering the unknown disturbance, the proportional and multiple integral (PMI) fault detection filter (FDF) is proposed for the state and input estimation. A structure including two residuals are employed to detect and isolate the faults of the proposed faulty sensors. Finally, the performance of the proposed methodology is evaluated through flight experiments of the UAV.