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A 'Model-on-Demand' Identification Methodology for Nonlinear Process Systems
Arizona State University, USA.
Arizona State University, USA.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2001 (English)In: International Journal of Control, ISSN 0020-7179, E-ISSN 1366-5820, Vol. 74, no 18, 1708-1717 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2001. Vol. 74, no 18, 1708-1717 p.
Keyword [en]
Identification, Multi-level pseudo-random sequence, Model-on-demand, Estimation, Performance
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-95678DOI: 10.1080/00207170110089734OAI: diva2:637230
Available from: 2013-07-17 Created: 2013-07-17 Last updated: 2013-07-17

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ReferencesLink to record
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