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
收藏
页码：761 / 766
页数：6
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