Projection based MIMO control performance monitoring : I - covariance monitoring in state space

Christopher A. MCNABB, S. Joe QIN*

*Corresponding author for this work

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

64 Citations (Scopus)

Abstract

In this paper we propose a new control performance monitoring method based on subspace projections. We begin with a state space model of a generally non-square process and derive the minimum variance control (MVC) law and minimum achievable variance in a state feedback form. We derive a multivariate time delay (MTD) matrix for use with our extended state space formulation, which implicitly is equivalent to the interactor matrix. We show how the minimum variance output space can be considered an optimal subspace of the general closed-loop output space and 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 a few simulated examples and an industrial wood waste burning power boiler. © 2003 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)739-757
Number of pages19
JournalJournal of Process Control
Volume13
Issue number8
Early online date2 May 2003
DOIs
Publication statusPublished - Dec 2003
Externally publishedYes

Bibliographical note

Financial support for this work from the National Science Foundation under CTS-9814340, Texas Higher Education Coordinating Board, and Weyerhaeuser Company through sponsorship of the Texas Modeling and Control Consortium is gratefully acknowledged.

Keywords

  • Control performance monitoring
  • Covariance monitoring
  • Minimum variance
  • Principal component analysis

Fingerprint

Dive into the research topics of 'Projection based MIMO control performance monitoring : I - covariance monitoring in state space'. Together they form a unique fingerprint.

Cite this