Robust Kalman Filter for Linear System With Convex Polytopic Uncertainties

被引：5

作者：

Yu, Xingkai ^{[1
]}

Xin, Dongjin ^{[2
,3
]}

Li, Jianxun ^{[4
]}

机构：

[1] Tsinghua Univ, Dept Precis Instrument, Beijing 100084, Peoples R China

[2] Xidian Univ, Sch Elect Engn, Minist Educ, Xian 710071, Peoples R China

[3] Xidian Univ, Key Lab High Speed Circuit Design & EMC, Minist Educ, Xian 710071, Peoples R China

[4] Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2023年 / 70卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Uncertainty; Kalman filters; Stochastic processes; Mathematical models; Circuits and systems; Symmetric matrices; Measurement uncertainty; Kalman filter; convex polytopic uncertain parameter; robust variance-constrained; DESIGN; ALGORITHM;

D O I：

10.1109/TCSII.2022.3170911

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 brief considers robust Kalman filtering problem for linear discrete-time systems with convex poly-topic uncertain parameters. First, we characterize the uncertain parameter matrices as a combination of several vertex matrices. Then, based on the mean square stability and H-2 and variance-constrained performance criteria, we design a variance-constrained based robust Kalman filter. The parametric matrices of the filter can be directly obtained by the variance-constrained optimization and MATLAB Toolbox. A classical instance is provided to verify the effectiveness of the proposed filter.

引用

页码：821 / 825

页数：5

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