A method of LPV model identification for control

Y. Zhu, Zuhua Xu

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

56 Citations (Scopus)

Abstract

Nonlinear process identification for control is studied. In identification test, the process is only tested (excited) along its operating-trajectory that includes various working points and transition periods. In model identification, a linear parameter varying (LPV) model is used. First linear models are identified using data sets at various working-points exclusive transition data; then the LPV model is identified by interpolating the linear models using total data. Sufficient conditions for a unique solution in parameter estimation will be given. Simulation study will be used to verify the effectiveness of the method. The identified model is suitable for model predictive control (MPC). 1.
Original languageEnglish
Title of host publicationProceedings of the 17th IFAC World Congress, July 6 -11 2008, Seoul, South Korea
EditorsH. Shim, P.M.M.J. Chung, Hyung Suck Cho
Place of PublicationOxford
PublisherPergamon
Pages5018-5023
ISBN (Print)978-3-902661-00-5
Publication statusPublished - 2008

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