Model predictive control for nonlinear batch processes with asymptotically perfect tracking

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dc.contributor.authorLee, KSko
dc.contributor.authorLee, JayHyungko
dc.date.accessioned2013-03-02T12:19:18Z-
dc.date.available2013-03-02T12:19:18Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued1997-
dc.identifier.citationCOMPUTERS CHEMICAL ENGINEERING, v.21, pp.873 - 879-
dc.identifier.issn0098-1354-
dc.identifier.urihttp://hdl.handle.net/10203/73489-
dc.description.abstractWe propose a novel model predictive control(MPC) algorithm designed specifically for nonlinear batch or other repetitive processes. Unlike existing MPCs which duplicate the same control error in the repeated batches, the proposed algorithm can achieve perfect tracking (for square systems) despite model uncertainty as the number of batch runs increases. As a special case, we also propose a maximum horizon algorithm which ensures convergence just as infinite horizon MPC does for continuous processes. Numerical examples are provided to demonstrate the performance of the proposed batch MPC algorithm.-
dc.languageEnglish-
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD-
dc.titleModel predictive control for nonlinear batch processes with asymptotically perfect tracking-
dc.typeArticle-
dc.identifier.wosidA1997XD31300144-
dc.identifier.scopusid2-s2.0-0042824923-
dc.type.rimsART-
dc.citation.volume21-
dc.citation.beginningpage873-
dc.citation.endingpage879-
dc.citation.publicationnameCOMPUTERS CHEMICAL ENGINEERING-
dc.identifier.doi10.1016/S0098-1354(97)00159-2-
dc.contributor.localauthorLee, JayHyung-
dc.contributor.nonIdAuthorLee, KS-
dc.type.journalArticleArticle; Proceedings Paper-
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