Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/4462
Title: Support vector machines-based generalized predictive control
Authors: İplikçi, Serdar
Keywords: Generalized predictive control
Modelling and prediction
Support vector machines
Algorithms
Computer simulation
Global optimization
Mathematical models
Nonlinear control systems
Problem solving
Regression analysis
Convex optimization problem
Function minimization block
Predictive control systems
Abstract: In this study, we propose a novel control methodology that introduces the use of support vector machines (SVMs) in the generalized predictive control (GPC) scheme. The SVM regression algorithms have extensively been used for modelling nonlinear systems due to their assurance of global solution, which is achieved by transforming the regression problem into a convex optimization problem in dual space, and also their higher generalization potential. These key features of the SVM structures lead us to the idea of employing a SVM model of an unknown plant within the GPC context. In particular, the SVM model can be employed to obtain gradient information and also it can predict future trajectory of the plant output, which are needed in the cost function minimization block. Simulations have confirmed that proposed SVM-based GPC scheme can provide a noticeably high control performance, in other words, an unknown nonlinear plant controlled by SVM-based GPC can accurately track the reference inputs with different shapes. Moreover, the proposed SVM-based GPC scheme maintains its control performance under noisy conditions. Copyright © 2006 John Wiley & Sons, Ltd.
URI: https://hdl.handle.net/11499/4462
https://doi.org/10.1002/rnc.1094
ISSN: 1049-8923
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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