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Adaptive unscented Kalman filter for estimation of modelling errors for helicopter
Song Q(宋崎); He YQ(何玉庆)
Department机器人学研究室
Conference Name2009 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Conference DateDecember 19-23, 2009
Conference PlaceGuilin, China
Author of SourceIEEE Robotics and Automation Society; National University of Defense Technology; The Chinese University of Hong Kong; City University of Hong Kong
Source Publication2009 IEEE International Conference on Robotics and Biomimetics (ROBIO)
PublisherIEEE Computer Society
Publication PlacePiscataway, NJ, USA
2009
Pages2463-2467
Indexed ByEI ; CPCI(ISTP)
EI Accession number20101712895196
WOS IDWOS:000285530501213
Contribution Rank1
ISBN978-1-4244-4775-6
AbstractIn 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.
Language英语
Citation statistics
Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/8547
Collection机器人学研究室
Affiliation1.Automation Department, Shenyang Institute of Aeronautical Engineering, 37# Daoyi South street, Shenyang, 110136, China
2.Robotics Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences, China
Recommended Citation
GB/T 7714
Song Q,He YQ. Adaptive unscented Kalman filter for estimation of modelling errors for helicopter[C]//IEEE Robotics and Automation Society; National University of Defense Technology; The Chinese University of Hong Kong; City University of Hong Kong. Piscataway, NJ, USA:IEEE Computer Society,2009:2463-2467.
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