Open AccessExperimental Investigation for RUAV's Actuator Fault Detections with AESMF
Author(s) -
Dalei Song,
Juntong Qi,
Yang Lee,
Jianda Han
Publication year - 2015
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/60854
Subject(s) - actuator , computer science , control theory (sociology) , kalman filter , filter (signal processing) , fault detection and isolation , noise (video) , extended kalman filter , fault (geology) , stability (learning theory) , artificial intelligence , control (management) , machine learning , computer vision , seismology , image (mathematics) , geology
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