A 'model-on-demand' identification methodology for non-linear process systems

M. W. Braun, Daniel Rivera, A. Stenman

Research output: Contribution to journalArticle

69 Scopus citations

Abstract

An identification methodology based on multi-level pseudo-random sequence (multi-level PRS) input signals and 'Model-on-Demand' (MOD) estimation is presented for single-input, single-output non-linear process applications. 'Model-on-Demand' estimation allows for accurate prediction of non-linear systems while requiring few user choices and without solving a non-convex optimization problem, as is usually the case with global modelling techniques. By allowing the user to incorporate a priori information into the specification of design variables for multi-level PRS input signals, a sufficiently informative input-output dataset for MoD estimation is generated in a 'plant-friendly' manner. The usefulness of the methodology is demonstrated in case studies involving the identification of a simulated rapid thermal processing (RTP) reactor and a pilot-scale brine-water mixing tank. On the resulting datasets, MoD estimation displays performance comparable to that achieved via semi-physical modelling and semi-physical modelling combined with neural networks. The MoD estimator, however, achieves this level of performance with substantially lower engineering effort.

Original languageEnglish (US)
Pages (from-to)1708-1717
Number of pages10
JournalInternational Journal of Control
Volume74
Issue number18
DOIs
StatePublished - Dec 15 2001

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications

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