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Number 1 - March 2015
Volume 25 - 2015
Simultaneous state and parameter estimation based actuator fault detection and diagnosis for an unmanned helicopter
Chong Wu, Juntong Qi, Dalei Song, Xin Qi, Jianda Han
Abstract
Simultaneous state and parameter estimation based actuator fault detection and diagnosis (FDD) for single-rotor unmanned
helicopters (UHs) is investigated in this paper. A literature review of actuator FDD for UHs is given firstly. Based on
actuator healthy coefficients (AHCs), which are introduced to represent actuator faults, a combined dynamic model is
established with the augmented state containing both the flight state and AHCs. Then the actuator fault detection and
diagnosis problem is transformed into a general nonlinear estimation one: given control inputs and the measured flight
state contaminated by measurement noises, estimate both the flight state and AHCs recursively in each time-step, which
is also known as the simultaneous state and parameter estimation problem. The estimated AHCs can further be used for
fault tolerant control (FTC). Based on the existing widely used nonlinear estimation methods such as the unscented Kalman
filter (UKF) and the extended set-membership filter (ESMF), three kinds of adaptive schemes (KF-UKF, MIT-UKF and
MIT-ESMF) are proposed by our team to improve the actuator FDD performance. A comprehensive comparative study
on these different estimation methods is given in detail to illustrate their advantages and disadvantages when applied to
unmanned helicopter actuator FDD.
Keywords
actuator fault detection and diagnosis, unmanned helicopter, Kalman filter, set-membership filter, adaptive scheme