Adaptive three-step kalman filter for air data sensor fault detection and diagnosis

Abstract

© Copyright 2015 by Peng Lu. Air data sensor fault detection and diagnosis is important for the safety of aircraft. In this paper, first an extension of the robust three-step Kalman filter to nonlinear systems is made by proposing a robust three-step unscented Kalman filter. Therobust three-step unscented Kalman filter is found to be sensitive to the initial condition error when dealing with air data sensor fault estimation. A theoretical analysis of this sensitivity ispresented and a novel adaptive three-step unscented Kalman filter is proposed which is able tocope with not only the estimation of the air data sensor faults, but also the detection and isolation of faults. The adaptive three-step unscented Kalman filter contains three steps: time update, fault estimation and measurement update. This approach can reduce the sensitivity to the initial condition error. Finally, the air data sensor fault detection and diagnosis performance of the adaptive three-step unscented Kalman filter is validated using simulated aircraft data. Additionally, its performance is further validated using real flight-test data to demonstrate its performance under realistic uncertainties and disturbances. The results using both the simulated data and real flight-test data demonstrate the satisfactory fault detection and diagnosis performance of the adaptive three-step unscented Kalman filter and verify that it can be applied in practice to enhance the safety of aircraft.