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Minimum-Variance Assessment of Multivariable Control Systems

  • Mohieddine Jelali
Part of the Advances in Industrial Control book series (AIC)

Abstract

Since the loops in multivariable control systems can be coupled, a multivariable control strategy can further reduce process variations, thus, only multivariable assessment can provide the right measure of performance improvement potential in the general case. In this chapter, methods for multivariable minimum-variance benchmarking are presented: it is shown how to use the interactor matrix to derive the multivariable variant of MVC; then the FCOR algorithm as the most known algorithm for assessing MIMO control systems based on routine operating data and the knowledge of the interactor matrix is presented. As the interactor matrix is hard to determine, and thus control assessment based on it is difficult, an assessment procedure that does not require the interactor matrix is proposed. Numerous examples are given to illustrate how the methods work.

Keywords

Performance Index Transfer Function Matrix Markov Parameter Miso System Dither Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Mohieddine Jelali
    • 1
  1. 1.Faculty of Plant, Energy and Machine SystemsCologne University of Applied SciencesKoelnGermany

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