Adaptive unscented Kalman filter for estimation of modelling errors for helicopter

Authors:
Qi Song;Yuqing He
Affiliations:
Automation Department, Shenyang Institute of Aeronautical Engineering, Shenyang, China;Robotics Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences, China
Venue:
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Year:
2009

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Abstract

In order to overcome the drawback of the normal unscented Kalman filter (UKF) a novel adaptive UKF (AUKF) is developed and applied to nonlinear joint estimation of both time-varying states and modelling errors for helicopter. The filter is composed of two parallel master-slave UKFs, while the master UKF estimates the states/parameters and the slave one estimates the diagonal elements of the noise covariance matrix for the master UKF. Such a mechanism improves the adaptive ability of the UKF and enlarges its application scope. Simulations conducted on the dynamics of helicopter indicate that the performance of the adaptive UKF is superior to the standard one in terms of fast convergence and estimation accuracy.