Adaptive unscented Kalman filter for estimation of modelling errors for helicopter

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	Adaptive unscented Kalman filter for estimation of modelling errors for helicopter
	Song Q(宋崎); He YQ(何玉庆)
作者部门	机器人学研究室
会议名称	2009 IEEE International Conference on Robotics and Biomimetics (ROBIO)
会议日期	December 19-23, 2009
会议地点	Guilin, China
会议主办者	IEEE Robotics and Automation Society; National University of Defense Technology; The Chinese University of Hong Kong; City University of Hong Kong
会议录名称	2009 IEEE International Conference on Robotics and Biomimetics (ROBIO)
出版者	IEEE Computer Society
出版地	Piscataway, NJ, USA
	2009
页码	2463-2467
收录类别	EI ; CPCI（ISTP）
EI收录号	20101712895196
WOS记录号	WOS:000285530501213
产权排序	1
ISBN号	978-1-4244-4775-6
摘要	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.
语种	英语
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	会议论文
条目标识符	http://ir.sia.cn/handle/173321/8547
专题	机器人学研究室
作者单位	1.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
推荐引用方式 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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