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AuthorGolabi, Arash
AuthorMeskin, Nader
AuthorToth, Roland
AuthorMohammadpour, Javad
Available date2020-09-24T08:11:57Z
Publication Date2017
Publication NameIEEE Transactions on Control Systems Technology
ResourceScopus
ISSN10636536
URIhttp://dx.doi.org/10.1109/TCST.2016.2642159
URIhttp://hdl.handle.net/10576/16287
AbstractObtaining mathematical models that can accurately describe nonlinear dynamics of complex processes and be further used for model-based control design is a challenging task. In this brief, a Bayesian approach is introduced for data-driven identification of linear parameter-varying regression models in an input-output dynamic representation form with an autoregressive with exogenous variable (ARX) noise structure. The applicability of the proposed approach is then investigated for the modeling of complex nonlinear process systems. In this approach, the dependence structure of the model on the scheduling variables is identified based on a Gaussian process (GP) formulation. The GP is used as a prior distribution to describe the possible realization of the scheduling-dependent coefficient functions of the estimated model. Then, a posterior distribution of these functions is obtained given the measured data and the mean value of this distribution is used to determine the estimated model. The properties and performance of the proposed method are evaluated using an illustrative example of a chemical process, namely, a distillation column, as well as an experimental case study with a three tank system. 1 2017 IEEE.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectBayesian method
Gaussian process (GP)
high-purity distillation column
linear parameter-varying (LPV) models
system identification
three tank system
TitleA Bayesian Approach for LPV Model Identification and Its Application to Complex Processes
TypeArticle
Pagination2160-2167
Issue Number6
Volume Number25
dc.accessType Abstract Only


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