Abstract
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.
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References
Rouhani, R., Mehra, R.K.: Model Algorithmic control (MAC), Basic Theoretical Properties. Automatica 18(4), 401–414 (1982)
Cutler, C.R., Ramaker, B.L.: Dynamic Matrix Control—A Computer Control Algorithm. In: Proc. JACC, SanFranciso (1980)
Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1995)
Shu, D.Q.: Predictive control system and its application. China Machine Press, Beijing (1996)
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhong, W., Pi, D., Sun, Y. (2005). SVM Based Nonparametric Model Identification and Dynamic Model Control. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_93
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DOI: https://doi.org/10.1007/11539087_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
eBook Packages: Computer ScienceComputer Science (R0)