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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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