Adaptive Robust UKF for Nonlinear Systems with Parameter Uncertainties

被引:0
|
作者
Ishihara, Shinji [1 ,2 ]
Yamakita, Masaki [2 ]
机构
[1] Hitachi Ltd, Res & Dev Grp, Ctr Technol Innovat Mech Engn, Ibaraki, Japan
[2] Tokyo Inst Technol, Dept Mech & Control Engn, Tokyo, Japan
关键词
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暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper addresses robust filtering for nonlinear systems with parameter uncertainties. We developed a new robust unscented Kalman filter (RUKF) which doesn't require calculating Jacobian matrix by using Unscented Statistical Linearization to consider the influence of parameter uncertainties of covariance matrices. The RUKF is more accurate than conventional UKF when the systems have parameter uncertainties. However, when there is no parameter uncertainty, estimation accuracy of the RUKF may be inferior to that of the UKF. Then, we also developed adaptive RUKF (ARUKF) by introducing an adaptive scheme into RUKF to automatically tune the influence of parameter uncertainties. The validity of the proposed methods is illustrated by Monte Carlo simulations.
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页码:48 / 53
页数:6
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