Extended State based Kalman Filter for Uncertain Systems with Bias

被引：2
|
作者：
Zhang, Xiaocheng [1 ,2 ]
Xue, Wenchao [1 ,2 ]
Fang, Haitao [1 ,2 ]
Li, Shihua [3 ]
Yang, Jun [3 ]
机构：
[1] Chinese Acad Sci, Acad Math & Syst Sci, LSC, NCMIS, Beijing, Peoples R China
[2] Univ Chinese Acad Sci, Sch Math Sci, Beijing, Peoples R China
[3] Southeast Univ, Sch Automat, Nanjing, Peoples R China
来源：
IFAC PAPERSONLINE | 2020年 / 53卷 / 02期
基金：
国家重点研发计划;
关键词：
Extended state observer; Kalman filter; Uncertain estimation; Measurement bias; DISTURBANCE REJECTION;
D O I：
10.1016/j.ifacol.2020.12.018
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper addresses the state estimation for a class of stochastic systems with both uncertain dynamics and measurement bias. By using the idea of uncertainty/disturbance estimation, an extended state based Kalman filter algorithm is developed to estimate the original state, the uncertain dynamics and the measurement bias. Furthermore, a necessary and sufficient condition for the observability of augmented system is presented. Also, the stability of the proposed algorithm is analyzed. It is shown that the proposed filter can achieve unbiased estimation of measurement bias, such that the influence of measurement bias is eliminated. Finally, a simulation study is provided to illustrate the effectiveness of proposed method. Copyright (C) 2020 The Authors.
引用
收藏
页码：2299 / 2304
页数：6
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