Refined instrumental variable methods for identification of LPV Box-Jenkins models

被引：120

作者：

Laurain, Vincent ^{[1
]}

Gilson, Marion ^{[1
]}

Toth, Roland ^{[2
]}

Garnier, Hugues ^{[1
]}

机构：

[1] Nancy Univ, CRAN, CNRS, F-54506 Vandoeuvre Les Nancy, France

[2] Delft Univ Technol, DCSC, NL-2628 CD Delft, Netherlands

来源：

AUTOMATICA | 2010年 / 46卷 / 06期

关键词：

LPV models; System identification; Refined instrumental variable; Box-Jenkins models; Input/ouput; Transfer function;

D O I：

10.1016/j.automatica.2010.02.026

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The identification of linear parameter-varying systems in an input-output setting is investigated, focusing on the case when the noise part of the data generating system is an additive colored noise. In the Box-Jenkins and output-error cases, it is shown that the currently available linear regression and instrumental variable methods from the literature are far from being optimal in terms of bias and variance of the estimates. To overcome the underlying problems, a refined instrumental variable method is introduced. The proposed approach is compared to the existing methods via a representative simulation example. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：959 / 967

页数：9

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