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

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 determinis...

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Bibliographic Details
Published inChinese journal of chemical engineering Vol. 20; no. 6; pp. 1136 - 1141
Main Author 刘涛 邵诚
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2012
Subjects
Chemical engineering
closed-loop error-in-variables system
Decomposition
extended observability matrix
Identification methods
Mathematical analysis
Matrices
Matrix methods
Noise
orthogonal projection
Principal component analysis
Projection
Proving
subspace identification
Subspaces
主成分分析
变量系统
奇异值分解
子空间辨识方法
正交投影
消除噪声
状态空间模型
闭环
subspace identification
extended observability matrix
orthogonal projection
closed-loop error-in-variables system
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Summary: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.
Bibliography:LIU Tao ＊＊ and SHAO Cheng Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024, China
closed-loop error-in-variables system; subspace identification; extended observability matrix; orthogonal projection
11-3270/TQ
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.
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1004-9541
2210-321X
DOI:10.1016/S1004-9541(12)60598-9