An Extended Closed-loop Subspace Identification Method for Error-in-variables Systems*

Chinese Journal of Chemical Engineering ›› 2012, Vol. 20 ›› Issue (6): 1136-1141.

An Extended Closed-loop Subspace Identification Method for Error-in-variables Systems^*

刘涛, 邵诚

Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024, China

收稿日期:2012-04-27 修回日期:2012-07-26 出版日期:2012-12-28 发布日期:2012-12-28
通讯作者: LIU Tao,E-mail:liurouter@ieee.org;SHAO Cheng,E-mail:cshao@dlut.edu.cn
基金资助:
Supported in part by Chinese Recruitment Program of Global Young Expert;Alexander von Humboldt Research Fellowship of Germany;the Foundamental Research Funds for the Central Universities;the National Natural Science Foundation of China (61074020)

An Extended Closed-loop Subspace Identification Method for Error-in-variables Systems^*

LIU Tao, SHAO Cheng

Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024, China

Received:2012-04-27 Revised:2012-07-26 Online:2012-12-28 Published:2012-12-28
Supported by:
Supported in part by Chinese Recruitment Program of Global Young Expert;Alexander von Humboldt Research Fellowship of Germany;the Foundamental Research Funds for the Central Universities;the National Natural Science Foundation of China (61074020)

摘要/Abstract

摘要： A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations,known as the error-in-variables (EIV) problem.Using the orthogonal projection approach to eliminate the noise influence,consistent estimation is guaranteed for the deterministic part of such a system.A strict proof is given for analyzing the rank condition for such orthogonal projection,in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and lower triangular Toeliptz matrix of the plant state-space model.In the result,the plant state matrices can be retrieved in a transparent manner from the above matrices.An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.

关键词: closed-loop error-in-variables system, subspace identification, extended observability matrix, orthogonal projection

Abstract: A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations,known as the error-in-variables (EIV) problem.Using the orthogonal projection approach to eliminate the noise influence,consistent estimation is guaranteed for the deterministic part of such a system.A strict proof is given for analyzing the rank condition for such orthogonal projection,in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and lower triangular Toeliptz matrix of the plant state-space model.In the result,the plant state matrices can be retrieved in a transparent manner from the above matrices.An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.

Key words: closed-loop error-in-variables system, subspace identification, extended observability matrix, orthogonal projection

刘涛, 邵诚. An Extended Closed-loop Subspace Identification Method for Error-in-variables Systems^*[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1136-1141.

LIU Tao, SHAO Cheng. An Extended Closed-loop Subspace Identification Method for Error-in-variables Systems^*[J]. Chin.J.Chem.Eng., 2012, 20(6): 1136-1141.

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An Extended Closed-loop Subspace Identification Method for Error-in-variables Systems^*

An Extended Closed-loop Subspace Identification Method for Error-in-variables Systems^*

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