Fault Diagnosis For Flight Control System Sensor Based On STF

In UAV flight control system, the sensor is used to measure the state parameters of theaircraft, and provide feedback to the flight control computer for flight control law solver,enabling the autopilot mission of UAV. Fault diagnosis for UAV flight control system is animportant research content of UAV in health management. As the sensor is the mostfailure-prone areas of flight control system, making timely and correct prediction of its failureis particularly important.A method of the strong tracking Kalman filter for UAV flight control system of sensorfault diagnosis was presented in this thesis. First of all, in the existed fault diagnosistechniques, according to the characteristics of sensors in the flight control system, theadvantages of the fault diagnosis technology of the strong tracking Kalman filter are putforward. Secondly, through the understanding of the UAV flight control system structure andits flight control system sensors, with researching and analysing the flight control systemmathematical model, the principle and derivation method of strong tracking filter is studiedand a solid foundation for what follows is laid. What’s more, the parameters of the flightcontrol system are expanded to the state variables, and the sensor fault observer is constructed,then the joint estimation of flight control system status and parameters are obtained. It couldbe seen from the simulation results that this method can estimate the state and fault diagnosisparameters of the sensors in the flight control system real-timely. Finally, the algorithmimprovement of strong tracking Kalman filter for sensor fault diagnosis is proposed. Throughthe wavelet filtering processing for the estimated value(b(k)) in Strong Tracking Kalmanfilter, relatively smooth estimates b^(k)is produced, then according to b(k), the sensor canbe quickly detected whether a failure has occurred, and by the use of^b(k), the accurate fault amplitude can be measured. Through the experimental simulation proves the validity of themethod.