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Erschienen in:
2005 | OriginalPaper | Buchkapitel
SVM Based Nonparametric Model Identification and Dynamic Model Control
verfasst von : Weimin Zhong, Daoying Pi, Youxian Sun
Erschienen in: Advances in Natural Computation
Verlag: Springer Berlin Heidelberg
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In this paper, a support vector machine (SVM) with linear kernel function based nonparametric model identification and dynamic matrix control (SVM_DMC) technique is presented. First, a step response model involving manipulated variables is obtained via system identification by SVM with linear kernel function according to random test data or manufacturing data. Second, an explicit control law of a receding horizon quadric objective is gotten through the predictive control mechanism. Final, the approach is illustrated by a simulation of a system with dead time delay. The results show that SVM_DMC technique has good performance in predictive control with good capability in keeping reference trajectory.