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State space ls-svm for polynomial nonlinear state space model based generalized predictive control of nonlinear systems

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dc.contributor.author Dilmen, Erdem
dc.contributor.author Beyhan, Selami
dc.date.accessioned 2019-08-16T13:31:55Z
dc.date.available 2019-08-16T13:31:55Z
dc.date.issued 2018
dc.identifier.issn 9781538676981 (ISBN)
dc.identifier.uri http://acikerisim.pau.edu.tr:8080/xmlui/handle/11499/10617
dc.description.abstract This paper proposes a novel state space least squares support vector machine (SS LS-SVM) for polynomial nonlinear state space (PNLSS) model based recursive system identification. SS LS-SVM, which also possesses an adaptive kernel function, provides an optimum formulation of the monomials (ζ) of the states and input in the PNLSS model. Hence, the PNLSS model encompasses the proposed SS LS-SVM. Recursive nonlinear state space identification is developed in the output error prediction context. The input-output observations are processed sequentially, hence leading to recursive update of the parameters using conventional Gauss-Newton optimization. System states do not need to be measured. However, to to yield a conformal representation of the actual system, number of states need to be known via some physical insight. This characterizes the identification procedure as a grey box one. The PNLSS model is employed in the generalized predictive control (GPC) of a nonlinear continuously stirred tank reactor (CSTR) system. The case which includes additive white noise on the output measurements and a time-varying parameter in the nonlinear system is considered. Numerical applications give the results of a high closed loop identification performance addition to the smooth control input and closely tracking the reference in the GPC scheme. © 2018 IEEE.
dc.language.iso English
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.relation.isversionof 10.1109/CCTA.2018.8511434
dc.rights info:eu-repo/semantics/closedAccess
dc.subject Nonlinear systems
dc.subject Predictive control systems
dc.subject State space methods
dc.subject Support vector machines
dc.subject Vector spaces
dc.subject White noise
dc.subject Adaptive kernel functions
dc.subject Closed loop identification
dc.subject Continuously stirred tank reactor
dc.subject Gauss-Newton optimization
dc.subject Generalized predictive control
dc.subject Identification procedure
dc.subject Least squares support vector machines
dc.subject Nonlinear state space models
dc.subject Model predictive control
dc.title State space ls-svm for polynomial nonlinear state space model based generalized predictive control of nonlinear systems
dc.type Conference Paper
dc.identifier.startpage 324
dc.identifier.endpage 330
dc.relation.publicationCategory Uluslararası Hakemli Dergi
dc.identifier.index Scopus
dc.identifier.index WOS

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