Robust estimation and filtering in uncertain linear systems under unknown covariations

被引：7
|
作者：
Kogan, M. M. [1 ]
机构：
[1] Nizhny Novgorod State Univ Architecture & Civil E, Nizhnii Novgorod, Russia
来源：
AUTOMATION AND REMOTE CONTROL | 2015年 / 76卷 / 10期
基金：
俄罗斯基础研究基金会;
关键词：
Covariance; Remote Control; Covariance Matrice; Random Factor; Linear Matrix Inequality;
D O I：
10.1134/S0005117915100033
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In the problems of estimation and filtering under uncertainty in the regressors, parameters, and covariances of random noise and perturbations, the best possible upper boundary of the proportionality coefficient between the root-mean-square error of estimate or filter and the sum of variances of all random factors was determined. This boundary which was named the level of suppression of random perturbations is characterized in terms of the linear matrix inequalities. The minimax estimate and minimax filter optimizing this index were established. The optimal robust estimate and filter were obtained using additional information about the membership of the covariance matrix in the given convex polyhedron.
引用
收藏
页码：1751 / 1764
页数：14
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