Closed-loop subspace identification using the parity space

Jin WANG; S. Joe QIN

doi:10.1016/j.automatica.2005.09.012

Closed-loop subspace identification using the parity space

Jin WANG, S. Joe QIN^*

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

104 Citations (Scopus)

Abstract

It is known that many subspace algorithms give biased estimates for closed-loop data due to the existence of feedback. In this paper we present a new subspace identification method using the parity space employed in fault detection in the past. The basic algorithm, known as subspace identification method via principal component analysis (SIMPCA), gives consistent estimation of the deterministic part and stochastic part of the system under closed loop. Column weighting for SIMPCA is introduced which shows improved efficiency/accuracy. A simulation example is given to illustrate the performance of the proposed algorithm in closed-loop identification and the effect of column weighting. © 2005 Elsevier Ltd. All rights reserved.

Original language	English
Pages (from-to)	315-320
Number of pages	6
Journal	Automatica
Volume	42
Issue number	2
Early online date	20 Dec 2005
DOIs	https://doi.org/10.1016/j.automatica.2005.09.012
Publication status	Published - Feb 2006
Externally published	Yes

Funding

Financial support from National Science Foundation under CTS-9985074 and an Overseas Young Investigator Award from NSF China (60228001) is gratefully acknowledged.

Keywords

Closed-loop identification
Consistency analysis
Instrumental variables
Parity space
Subspace identification

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10.1016/j.automatica.2005.09.012

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title = "Closed-loop subspace identification using the parity space",

abstract = "It is known that many subspace algorithms give biased estimates for closed-loop data due to the existence of feedback. In this paper we present a new subspace identification method using the parity space employed in fault detection in the past. The basic algorithm, known as subspace identification method via principal component analysis (SIMPCA), gives consistent estimation of the deterministic part and stochastic part of the system under closed loop. Column weighting for SIMPCA is introduced which shows improved efficiency/accuracy. A simulation example is given to illustrate the performance of the proposed algorithm in closed-loop identification and the effect of column weighting. {\textcopyright} 2005 Elsevier Ltd. All rights reserved.",

keywords = "Closed-loop identification, Consistency analysis, Instrumental variables, Parity space, Subspace identification",

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T1 - Closed-loop subspace identification using the parity space

AU - WANG, Jin

AU - QIN, S. Joe

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N2 - It is known that many subspace algorithms give biased estimates for closed-loop data due to the existence of feedback. In this paper we present a new subspace identification method using the parity space employed in fault detection in the past. The basic algorithm, known as subspace identification method via principal component analysis (SIMPCA), gives consistent estimation of the deterministic part and stochastic part of the system under closed loop. Column weighting for SIMPCA is introduced which shows improved efficiency/accuracy. A simulation example is given to illustrate the performance of the proposed algorithm in closed-loop identification and the effect of column weighting. © 2005 Elsevier Ltd. All rights reserved.

AB - It is known that many subspace algorithms give biased estimates for closed-loop data due to the existence of feedback. In this paper we present a new subspace identification method using the parity space employed in fault detection in the past. The basic algorithm, known as subspace identification method via principal component analysis (SIMPCA), gives consistent estimation of the deterministic part and stochastic part of the system under closed loop. Column weighting for SIMPCA is introduced which shows improved efficiency/accuracy. A simulation example is given to illustrate the performance of the proposed algorithm in closed-loop identification and the effect of column weighting. © 2005 Elsevier Ltd. All rights reserved.

KW - Closed-loop identification

KW - Consistency analysis

KW - Instrumental variables

KW - Parity space

KW - Subspace identification

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U2 - 10.1016/j.automatica.2005.09.012

DO - 10.1016/j.automatica.2005.09.012

M3 - Journal Article (refereed)

SN - 0005-1098

VL - 42

SP - 315

EP - 320

JO - Automatica

JF - Automatica

IS - 2

ER -

Closed-loop subspace identification using the parity space

Abstract

Funding

Keywords

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