Realistic disturbance modeling using Hidden Markov Models: Applications in model-based process control

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dc.contributor.authorWong, Wee Chinko
dc.contributor.authorLee, Jay H.ko
dc.date.accessioned2013-03-12T04:36:02Z-
dc.date.available2013-03-12T04:36:02Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2009-10-
dc.identifier.citationJOURNAL OF PROCESS CONTROL, v.19, no.9, pp.1438 - 1450-
dc.identifier.issn0959-1524-
dc.identifier.urihttp://hdl.handle.net/10203/101346-
dc.description.abstractUnderstanding and modeling disturbances play a critical part in designing effective advanced model-based control solutions. Existing linear, stationary disturbance models are oftentimes limiting in the face of time-varying characteristics typically witnessed in process industries. These include intermittent drifts, abrupt changes, temporary oscillations, outliers and the likes. This work proposes a Hidden Markov Model-based framework to deal with such situations that exhibit discrete, modal behavior. The usefulness of the proposed disturbance framework - from modeling to ensuring the integral action under a wide variety of scenarios - is demonstrated through several examples. (C) 2009 Elsevier Ltd. All rights reserved.-
dc.languageEnglish-
dc.publisherELSEVIER SCI LTD-
dc.subjectPREDICTIVE CONTROL-
dc.subjectLINEAR-MODELS-
dc.subjectSYSTEMS-
dc.subjectALGORITHM-
dc.titleRealistic disturbance modeling using Hidden Markov Models: Applications in model-based process control-
dc.typeArticle-
dc.identifier.wosid000271529200003-
dc.identifier.scopusid2-s2.0-72049084751-
dc.type.rimsART-
dc.citation.volume19-
dc.citation.issue9-
dc.citation.beginningpage1438-
dc.citation.endingpage1450-
dc.citation.publicationnameJOURNAL OF PROCESS CONTROL-
dc.identifier.doi10.1016/j.jprocont.2009.04.014-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorLee, Jay H.-
dc.contributor.nonIdAuthorWong, Wee Chin-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorDisturbance modeling-
dc.subject.keywordAuthorHidden Markov Models-
dc.subject.keywordPlusPREDICTIVE CONTROL-
dc.subject.keywordPlusLINEAR-MODELS-
dc.subject.keywordPlusSYSTEMS-
dc.subject.keywordPlusALGORITHM-
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