Experimental Investigation for RUAV's Actuator Fault Detections with AESMF

Abstract

The adaptive extended set-membership filter (AESMF) algorithm for robots' online modelling is today proposed for use in this field. Compared to the traditional ESMF, this novel filter method improves estimation accuracy under variable boundaries of unknown but bounded (UBB) process noise, which is often caused by the uncertainties of robotic dynamics. However, the applicability and stability of the AESMF method have not been tested in detail or demonstrated for real robotic systems. In this research, AESMF is applied for the actuator fault detections of a rotor-craft unmanned air vehicle (RUAV). The stability of AESMF is firstly analysed using mathematics and actuator healthy coefficients (AHC) are introduced for building the actuator failure model of RUAVs. AESMF is employed for the online boundary estimation of flight states and AHC parameters for fault tolerance control. Based on the proposed AESMF actuator fault estimation, flight experiments are conducted using a ServoHeli-40 RUAV platform and the flight results are compared with traditional ESMF and the adaptive extended Kalman filter (AEKF) in order to demonstrate its effectiveness, as well as for suggesting improvements for the actuator failure detection of RUAVs.