Actuator Faults Estimation for a Helicopter UAV in Presence of Disturbances

Abstract

The aim of this paper is to develop robust three stage extended Kalman filter (RThSEKF) for a model based on a fault detection and identification (FDI) for nonlinear hover mode system of helicopter unmanned aerial vehicle (HUAV). In addition, we show that, in considered systems, the actuator faults are affected by each other motivated in five scenarios simulation results. More precisely, the proposed approach estimates and decouples actuator faults in the presence of external disturbances in non-linear mathematical model. Moreover, we analyze and identify various faults such as bias fault and also catastrophic faults such as stuck and floating faults. Finally, the simulation results show effectiveness of the proposed robust method for detection and isolation of various actuator faults and differentiating bias and stuck faults.