A Subspace Approach to MIMO Control Performance Monitoring and Diagnosis

S. Joe QIN*, Christopher A. MCNABB

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review


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 languageEnglish
Pages (from-to)529-534
Number of pages6
JournalIFAC Proceedings Volumes
Issue number1
Publication statusPublished - Jan 2004
Externally publishedYes
Event7th International Symposium on Advanced Control of Chemical Processes, ADCHEM 2003 - , China
Duration: 11 Jan 200414 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.


  • Covariance-based monitoring
  • Generalized eigenanalysis
  • MIMO control performance monitoring


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