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CLC number: TP273.3; TP183

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Received: 2006-03-23

Revision Accepted: 2006-10-21

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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.5 P.748~754


LS-SVM model based nonlinear predictive control for MCFC system

Author(s):  CHEN Yue-hua, CAO Guang-yi, ZHU Xin-jian

Affiliation(s):  Institute of Fuel Cell, Department of Automation, Shanghai Jiao Tong University, Shanghai 200030, China

Corresponding email(s):   chenqi78@sjtu.edu.cn

Key Words:  Molten carbonate fuel cell (MCFC), Least squares support vector machine (LS-SVM), Genetic algorithm (GA), Nonlinear predictive controller

CHEN Yue-hua, CAO Guang-yi, ZHU Xin-jian. LS-SVM model based nonlinear predictive control for MCFC system[J]. Journal of Zhejiang University Science A, 2007, 8(5): 748~754.

@article{title="LS-SVM model based nonlinear predictive control for MCFC system",
author="CHEN Yue-hua, CAO Guang-yi, ZHU Xin-jian",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

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%T LS-SVM model based nonlinear predictive control for MCFC system
%A CHEN Yue-hua
%A CAO Guang-yi
%A ZHU Xin-jian
%J Journal of Zhejiang University SCIENCE A
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%P 748~754
%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A0748

T1 - LS-SVM model based nonlinear predictive control for MCFC system
A1 - CHEN Yue-hua
A1 - CAO Guang-yi
A1 - ZHU Xin-jian
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 5
SP - 748
EP - 754
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A0748

This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be controlled efficiently. First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control. And then, the optimal control sequences are obtained by applying genetic algorithm (GA). The model and controller have been realized in the MATLAB environment. Simulation results indicated that the proposed controller exhibits satisfying control effect.

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


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