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Robust Identification of Process Models from Plant Data
The University of Newcastle, Australia.
The University of Newcastle, Australia.
The University of Newcastle, Australia.
Universidad Técnica Federico Santa María.
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2008 (English)In: Journal of Process Control, ISSN 0959-1524, Vol. 18, no 9, 810-820 p.Article in journal (Refereed) Published
Abstract [en]

A precursor to any advanced control solution is the step of obtaining an accurate model of the process. Suitable models can be obtained from phenomenological reasoning, analysis of plant data or a combination of both. Here, we will focus on the problem of estimating (or calibrating) models from plant data. A key goal is to achieve robust identification. By robust we mean that small errors in the hypotheses should lead to small errors in the estimated models. We argue that, in some circumstances, it is essential that special precautions, including discarding some part of the data, be taken to ensure that robustness is preserved. We present several practical case studies to illustrate the results.

Place, publisher, year, edition, pages
Elsevier, 2008. Vol. 18, no 9, 810-820 p.
Keyword [en]
Closed loop identification; Robust identification
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-72586DOI: 10.1016/j.jprocont.2008.06.004ISI: 000260271800002OAI: oai:DiVA.org:kth-72586DiVA: diva2:488356
Note
QC 20120208Available from: 2012-02-01 Created: 2012-01-31 Last updated: 2012-02-08Bibliographically approved

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Rojas, Cristian R.

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