Cautious control of industrial process variability with uncertain input and disturbance model parameters

Daniel W. Apley*, Jeongbae Kim

*Corresponding author for this work

Research output: Contribution to specialist publicationArticle

19 Scopus citations

Abstract

This article discusses a method for controlling variation in industrial processes when the model parameters are estimated from data and subject to uncertainty. A static input/output relationship with multiple input variables and an integrated moving average disturbance model are assumed. Most robust control methods use deterministic measures of uncertainty and a control objective that focuses on worst-case performance. This work uses a probabilistic measure of uncertainty and a control objective that relates more closely to minimizing variation, where parameter estimation errors are treated simply as an additional source of variability. We show that this approach results in a higher probability of closed-loop stability than the standard minimum variance control and can substantially lessen the adverse impact of parameter uncertainty on closed-loop variance. Guidelines for designing and evaluating the experiment used to estimate the model parameters are also discussed.

Original languageEnglish (US)
Pages188-199
Number of pages12
Volume46
No2
Specialist publicationTechnometrics
DOIs
StatePublished - May 1 2004

Keywords

  • Cautious control
  • Engineering process control
  • Minimum variance control
  • Robust control
  • Statistical process control

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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