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Support vector machines‐based generalized predictive control
Author(s) -
Iplikci S.
Publication year - 2006
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.1094
Subject(s) - support vector machine , computer science , context (archaeology) , model predictive control , generalization , nonlinear system , mathematical optimization , block (permutation group theory) , artificial intelligence , dual (grammatical number) , least squares support vector machine , machine learning , control theory (sociology) , mathematics , control (management) , art , paleontology , mathematical analysis , physics , geometry , literature , quantum mechanics , biology
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.