Abstract
A model reference control algorithm based on Support Vector Machine (SVM) for discrete nonlinear systems is proposed in this article. It uses SVM as regression tools to learn the feed forward controller from input and output data. Further more, a compensator is used as the feed back controller to make the whole system more robust. Advantages of SVM and the robust compensator help the method perform excellently as shown in the experiments.
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References
Suykens, J.A.K., et al.: Optimal Control by Least Squares Support Vector Machine. Neural Networks 14, 23–35 (2001)
Zhong, Y.-S.: Robust output tracking control for SISO plants with multiple operating points and with parametric and unstructured uncertainties. INT.J.CONTROL 75, 219–241 (2002)
De Kruif, B.J., De Vries, T.J.A.: On using a support vector machine in Learning Feed- Forward Control. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Como Italy, vol. 1, pp. 272–277 (2001)
Irwin, G.W., Hunt, K.J., et al. (eds.): Neural network Applications In Control. The institution of Electrical Engineers (1992)
Butler, H. (ed.): Model Reference Adaptive Control- from theory to practice. Prentice-Hall, Englewood Cliffs (1992)
Vapnik, V.: Statistical learning theory. Wiley, New York (1998)
Widrow, B.: Adaptive Inverse Control. Prentice-Hall, Englewood Cliffs (1996)
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© 2004 Springer-Verlag Berlin Heidelberg
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He, J., Zhang, Z. (2004). Model Reference Control Based on SVM. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_21
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DOI: https://doi.org/10.1007/978-3-540-28648-6_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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