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Received: 2004-02-15

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Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.5 P.440~446

http://doi.org/10.1631/jzus.2005.A0440


New predictive control algorithms based on least squares Support Vector Machines


Author(s):  LIU Bin, SU Hong-ye, CHU Jian

Affiliation(s):  National Laboratory of Industrial Control Technology, Institute of Advanced Process Control, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   bliu@iipc.zju.edu.cn

Key Words:  Least squares Support Vector Machines, Linear kernel function, RBF kernel function, Generalized predictive control


LIU Bin, SU Hong-ye, CHU Jian. New predictive control algorithms based on least squares Support Vector Machines[J]. Journal of Zhejiang University Science A, 2005, 6(5): 440~446.

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author="LIU Bin, SU Hong-ye, CHU Jian",
journal="Journal of Zhejiang University Science A",
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%T New predictive control algorithms based on least squares Support Vector Machines
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0440

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T1 - New predictive control algorithms based on least squares Support Vector Machines
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2005.A0440


Abstract: 
Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on least squares Support Vector Machines (LS-SVM) model. For the weakly nonlinear system, the system model is built by using LS-SVM with linear kernel function, and then the obtained linear LS-SVM model is transformed into linear input-output relation of the controlled system. However, for the strongly nonlinear system, the off-line model of the controlled system is built by using LS-SVM with Radial Basis Function (RBF) kernel. The obtained nonlinear LS-SVM model is linearized at each sampling instant of system running, after which the on-line linear input-output model of the system is built. Based on the obtained linear input-output model, the generalized predictive control (GPC) algorithm is employed to implement predictive control for the controlled plant in both algorithms. The simulation results after the presented algorithms were implemented in two different industrial processes model respectively revealed the effectiveness and merit of both algorithms.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[3] Nie, J.H., Loh, A.P., Hang, C.C., 1996. Modeling pH neutralization processes using fuzzy-neutral approaches. Fuzzy Sets and Systems, 78:5-22.

[4] Rawlings, J.B., 2000. Tutorial overview of model predictive control. Control Systems Magazines, IEEE, 20(3):38-52.

[5] Smola, A.J., 1996. Regression Estimation with Support Vector Learning Machines. Master’s Thesis, Technische Universität München.

[6] Suykens, J.A.K., Vandewalle, J., 1999. Least Squares Support Vector Machine classifiers. Neural Processing Letters, 9(3):293-300.

[7] Vapnik, V., 1998. Statistical Learning Theory. John Wiley, New York, U.S.A.

[8] Yang, C.J., Sun, Y.X., Bao, B.L., 1997. A simplified GPC algorithm for pulp washing process. Mechanical & Electrical Engineering Magazine, 24(6):9-12.

[9] Zhang, X.G., 2000. Introduction to statistical learning theory and Support Vector Machines. ACTA AUTOMATICA SINICA, 26(1):32-42.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Amit@UFRJ<amit@nacad.ufrj.br>

2011-05-06 23:12:00

I intend to use your paper in a class I am teaching on so called intelligent control for presentation by students. Thanks.

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