Non-parametric identification of linear parameter-varying spatially-interconnected systems using an LS-SVM approach

Q. Liu, J. Mohammadpour, R. Toth, N. Meskin

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

2 Citations (Scopus)

Abstract

This paper considers a general approach for the identification of partial differential equation-governed spatially-distributed systems. Spatial discretization virtually divides a system into spatially-interconnected subsystems, which allows to define the identification problem at the subsystem level. Here we focus on such a distributed identification of spatially-interconnected systems with temporal/spatial varying properties, whose dynamics can be captured by temporal/spatial linear parameter-varying (LPV) models. Inaccurate selection of the functional dependencies of the model parameters on scheduling variables may lead to bias in the identified models. Hence, we propose a non-parametric identification approach via a least-squares support vector machine (LS-SVM) - `non-parametric' estimation is in the sense that the model dependence on the scheduling variables is not explicitly parametrized. The performance of the proposed approach is evaluated on an Euler-Bernoulli beam with varying thickness.
Original languageEnglish
Title of host publicationProceedings of the American Control Conference (ACC), 6-8 July 2016, Boston, Massachusetts
PublisherAmerican Automatic Control Council (AACC)
Pages4592-4597
ISBN (Print)978-1-4673-8682-1
DOIs
Publication statusPublished - 2016
Event2016 American Control Conference (ACC 2016), July 6-8, 2016, Boston, MA, USA - Boston Marriott Copley Place, Boston, MA, United States
Duration: 6 Jul 20168 Jul 2016
http://acc2016.a2c2.org/

Conference

Conference2016 American Control Conference (ACC 2016), July 6-8, 2016, Boston, MA, USA
Abbreviated titleACC 2016
CountryUnited States
CityBoston, MA
Period6/07/168/07/16
Internet address

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