Projection based MIMO control performance monitoring : II - Measured disturbances and setpoint changes

Christopher A. MCNABB, S. Joe QIN*

*Corresponding author for this work

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

20 Citations (Scopus)

Abstract

In this paper the performance monitoring method based on subspace projections from Part I [J. Proc. Cont. 13 (2003) 739] is extended to include measured disturbances and setpoint changes. It was shown in [J. Proc. Cont. 13 (2003) 739] that the minimum variance output space is an optimal subspace of the general closed-loop output space and that orthogonal projections of filtered output data onto past closed-loop output data can be used to assess the performance of feedback controllers. This paper demonstrates that the same framework is directly applicable to systems with measured disturbances by augmenting the data matrix with those measured disturbances. Furthermore, it provides a means of separating suboptimal control performance between that arising from unmeasured disturbances and that due to measured disturbances. The effect of setpoint changes on control performance can be calculated as special feedforward variables. The controller is generally time-varying to include the case of model predictive control. A simulation example and an industrial boiler process are used to demonstrate the effectiveness of the proposed method. © 2004 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)89-102
Number of pages14
JournalJournal of Process Control
Volume15
Issue number1
Early online date9 Jun 2004
DOIs
Publication statusPublished - Feb 2005
Externally publishedYes

Funding

Financial support for this work from the National Science Foundation under CTS-9985074 and Weyerhaeuser Company through sponsorship of the Texas–Wisconsin Modeling and Control Consortium is gratefully acknowledged.

Keywords

  • Covariance monitoring
  • Feedforward control
  • MIMO control performance monitoring
  • Minimum variance
  • Model predictive control

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