Robust Actuator Fault Detection and Diagnosis for a Quadrotor UAV With External Disturbances

This paper presents a robust actuator fault detection and diagnosis (FDD) scheme for a quadrotor UAV (QUAV) in the presence of external disturbances. First, the dynamic model of a QUAV taking into account actuator faults and external disturbances is constructed. Then, treating the actuator faults and external disturbances as augmented system states, an adaptive augmented state Kalman filter (AASKF), is developed without the need of make the assumption that the exact stochastic information of actuator faults and external disturbances are available. Next, in order to reduce the computational load of AASKF, an adaptive three-stage Kalman filter (AThSKF) is proposed by decoupling the AASKF into three subfilters. The AThSKF-based FDD scheme can not only detect and isolate actuator faults but also estimate the magnitudes even if the QUAV suffers from the external disturbances. Finally, the performance of the FDD scheme is evaluated under different fault scenarios, and simulation results demonstrate the effectiveness of the proposed method.