Some study on the identification of multi-model LPV models with two scheduling variables

J. Huang, J. Guoli, Y. Zhu, P.P.J. Bosch, van den

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

This work studies the identification of LPV (linear parameter varying) models with two scheduling variables in order to model complex industrial processes. The LPV model is parameterized as blended linear models, which is also called multi-model approach. Several weighting functions, linear, polynomial and Gaussian functions, are used and compared. The usefulness of the method is tested using a high purity distillation column model in a case study. The case study also shows that a good fit of identification data is not enough to verify model quality and can even be misleading in nonlinear process identification; other measures related to process knowledge should be used in model validation.
Original languageEnglish
Title of host publicationPreprints of the 16th IFAC Symposium on System Identification, 11-13 July 2012, Brussels, Belgium
Place of PublicationBrussels, Belgium
PublisherIFAC
Pages1269-1274
DOIs
Publication statusPublished - 2012
Event16th IFAC Symposium on System Identification (SYSID 2012) - Brussels, Belgium
Duration: 11 Jul 201213 Jul 2012

Conference

Conference16th IFAC Symposium on System Identification (SYSID 2012)
Abbreviated titleSYSID 2012
CountryBelgium
CityBrussels
Period11/07/1213/07/12

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