CLC number: TP273
On-line Access:
Received: 2004-02-15
Revision Accepted: 2004-06-15
Crosschecked: 0000-00-00
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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.
@article{title="New predictive control algorithms based on least squares Support Vector Machines",
author="LIU Bin, SU Hong-ye, CHU Jian",
journal="Journal of Zhejiang University Science A",
volume="6",
number="5",
pages="440-446",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.A0440"
}
%0 Journal Article
%T New predictive control algorithms based on least squares Support Vector Machines
%A LIU Bin
%A SU Hong-ye
%A CHU Jian
%J Journal of Zhejiang University SCIENCE A
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%P 440-446
%@ 1673-565X
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0440
TY - JOUR
T1 - New predictive control algorithms based on least squares Support Vector Machines
A1 - LIU Bin
A1 - SU Hong-ye
A1 - CHU Jian
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 5
SP - 440
EP - 446
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
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.
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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.