An Improved Fuzzy Kalman Filter for State Estimation of Nonlinear Systems

被引：1
|
作者：
Zhou, Zhi-Jie [1 ,2 ]
Hu, Chang-Hua [1 ]
Zhang, Bang-Cheng [3 ]
Chen, Liang [1 ]
机构：
[1] Hightech Inst Xian, Xian 710025, Shaanxi, Peoples R China
[2] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
[3] Changchun Univ Technol, Sch Electromech Engn, Seoul 130012, South Korea
来源：
ISND 2007: PROCEEDINGS OF THE 2007 INTERNATIONAL SYMPOSIUM ON NONLINEAR DYNAMICS, PTS 1-4 | 2008年 / 96卷
关键词：
D O I：
10.1088/1742-6596/96/1/012130
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The extended fuzzy Kalman filter (EFKF) is developed recently and used for state estimation of the nonlinear systems with uncertainty. Based on extension of the orthogonality principle and the extended fuzzy Kalman filter, an improved fuzzy Kalman filters (IFKF) is proposed in this paper, which is more applicable and can deal with the state estimation of the nonlinear systems better than the EFKF. A simulation study is provided to verify the efficiency of the proposed method.
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页数：7
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