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A closed-loop cross-correlation method for detecting model mismatch in MIMO model-based controllers.

Jonathan R Webber1, Yash P Gupta

  • 1Department of Process Engineering and Applied Science, Dalhousie University, Halifax, Nova Scotia, Canada B3J 1Z1. Jonathan.Webber@Fluor.com

ISA Transactions
|July 26, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel closed-loop cross-correlation method to detect model mismatch in multiple-input multiple-output (MIMO) control systems. The technique efficiently identifies specific input-output pairings needing re-identification, improving model accuracy.

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Area of Science:

  • Control Systems Engineering
  • System Identification
  • Automation

Background:

  • Model mismatch in control systems can lead to performance degradation.
  • Existing methods for detecting model mismatch are primarily limited to univariate systems.
  • Accurate system models are crucial for effective model-based control.

Purpose of the Study:

  • To extend model mismatch detection from univariate to multiple-input multiple-output (MIMO) control systems.
  • To present a closed-loop cross-correlation method for identifying specific mismatched input-output pairings.
  • To provide a tool for screening and selecting candidate models for re-identification.

Main Methods:

  • Utilizing the correlation between a dithering signal and prediction error.
  • Implementing a closed-loop cross-correlation approach for MIMO systems.
  • Identifying mismatched rows and columns in the transfer function matrix, followed by intersection analysis.
  • Employing partial control by holding manipulated variables constant to refine candidate models.

Main Results:

  • Successfully extended model mismatch detection to MIMO control systems.
  • Developed a method to pinpoint specific input-output pairings with model mismatch.
  • Demonstrated the utility of the method for model screening and candidate selection.
  • Showcased partial control as a strategy to reduce the set of candidate models.

Conclusions:

  • The proposed closed-loop cross-correlation method effectively detects model mismatch in MIMO systems.
  • This approach facilitates efficient identification of specific input-output pairings requiring re-identification.
  • The method serves as a valuable tool for improving the accuracy and reliability of model-based control systems.