An Introduction of a CCA Weighting Matrix to a Closed-Loop Subspace Identification Method

被引：0

作者：

Ikeda, Kenji ^{[1
]}

Tanaka, Hideyuki ^{[2
]}

机构：

[1] Tokushima Univ, Tokushima 7708506, Japan

[2] Hiroshima Univ, Higashihiroshima 7398524, Japan

来源：

IFAC PAPERSONLINE | 2021年 / 54卷 / 07期

关键词：

System identification; Subspace methods; Kalman filters; Semi-definite programming; CONSISTENCY ANALYSIS;

D O I：

10.1016/j.ifacol.2021.08.453

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 introduces a CCA (canonical correlation analysis) weighting matrix to an estimation method of the innovations model previously proposed by the authors. A numerical simulation illustrates that the CCA weighting reduces the covariance of the estimate and that the proposed method gives similar or better performance compared to Closed-Loop MOESP and PBSID. Especially, the design parameter called "past horizon" in the proposed method can be set small compared to Closed-Loop MOESP and PBSID. It is also analyzed how the bias of the estimate is reduced. Copyright (C) 2021 The Authors.

引用

页码：761 / 766

页数：6

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