A Subspace Approach to MIMO Control Performance Monitoring and Diagnosis

S. Joe QIN; Christopher A. MCNABB

doi:10.1016/S1474-6670(17)38786-4

A Subspace Approach to MIMO Control Performance Monitoring and Diagnosis

S. Joe QIN^*, Christopher A. MCNABB

^*Corresponding author for this work

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

Abstract

In this paper we begin with a state space model of a generally non-square process and derive the minimum achievable variance in a state feedback form. We propose a simple control performance calculation which uses orthogonal projection of filtered output data onto past closed-loop data. Finally, we propose a control performance monitoring technique based on the output covariance and diagnose the cause of suboptimal control performance using generalized eigenvector analysis. The proposed methods are demonstrated on an industrial wood waste burning power boiler.

Original language	English
Pages (from-to)	529-534
Number of pages	6
Journal	IFAC Proceedings Volumes
Volume	37
Issue number	1
DOIs	https://doi.org/10.1016/S1474-6670(17)38786-4
Publication status	Published - Jan 2004
Externally published	Yes
Event	7th International Symposium on Advanced Control of Chemical Processes, ADCHEM 2003 - , China Duration: 11 Jan 2004 → 14 Jan 2004

Bibliographical note

Financial support for this work from the National Science Foundation under CTS-0814340, Texas Higher Education Coordinating Board, and vVeyerhaeuser Company through sponsorship of the Texas Modeling and Control Consortium is gratefully acknowledged.

Keywords

Covariance-based monitoring
Generalized eigenanalysis
MIMO control performance monitoring

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10.1016/S1474-6670(17)38786-4

Cite this

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title = "A Subspace Approach to MIMO Control Performance Monitoring and Diagnosis",

abstract = "In this paper we begin with a state space model of a generally non-square process and derive the minimum achievable variance in a state feedback form. We propose a simple control performance calculation which uses orthogonal projection of filtered output data onto past closed-loop data. Finally, we propose a control performance monitoring technique based on the output covariance and diagnose the cause of suboptimal control performance using generalized eigenvector analysis. The proposed methods are demonstrated on an industrial wood waste burning power boiler.",

keywords = "Covariance-based monitoring, Generalized eigenanalysis, MIMO control performance monitoring",

author = "QIN, {S. Joe} and MCNABB, {Christopher A.}",

note = "Financial support for this work from the National Science Foundation under CTS-0814340, Texas Higher Education Coordinating Board, and vVeyerhaeuser Company through sponsorship of the Texas Modeling and Control Consortium is gratefully acknowledged.; 7th International Symposium on Advanced Control of Chemical Processes, ADCHEM 2003 ; Conference date: 11-01-2004 Through 14-01-2004",

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AU - QIN, S. Joe

AU - MCNABB, Christopher A.

N1 - Financial support for this work from the National Science Foundation under CTS-0814340, Texas Higher Education Coordinating Board, and vVeyerhaeuser Company through sponsorship of the Texas Modeling and Control Consortium is gratefully acknowledged.

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N2 - In this paper we begin with a state space model of a generally non-square process and derive the minimum achievable variance in a state feedback form. We propose a simple control performance calculation which uses orthogonal projection of filtered output data onto past closed-loop data. Finally, we propose a control performance monitoring technique based on the output covariance and diagnose the cause of suboptimal control performance using generalized eigenvector analysis. The proposed methods are demonstrated on an industrial wood waste burning power boiler.

AB - In this paper we begin with a state space model of a generally non-square process and derive the minimum achievable variance in a state feedback form. We propose a simple control performance calculation which uses orthogonal projection of filtered output data onto past closed-loop data. Finally, we propose a control performance monitoring technique based on the output covariance and diagnose the cause of suboptimal control performance using generalized eigenvector analysis. The proposed methods are demonstrated on an industrial wood waste burning power boiler.

KW - Covariance-based monitoring

KW - Generalized eigenanalysis

KW - MIMO control performance monitoring

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U2 - 10.1016/S1474-6670(17)38786-4

DO - 10.1016/S1474-6670(17)38786-4

M3 - Journal Article (refereed)

SN - 1474-6670

VL - 37

SP - 529

EP - 534

JO - IFAC Proceedings Volumes

JF - IFAC Proceedings Volumes

IS - 1

T2 - 7th International Symposium on Advanced Control of Chemical Processes, ADCHEM 2003

Y2 - 11 January 2004 through 14 January 2004

ER -

A Subspace Approach to MIMO Control Performance Monitoring and Diagnosis

Abstract

Bibliographical note

Keywords

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