CLC number: TP273.3; TP183
On-line Access:
Received: 2006-03-23
Revision Accepted: 2006-10-21
Crosschecked: 0000-00-00
Cited: 1
Clicked: 6890
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",
volume="8",
number="5",
pages="748-754",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A0748"
}
%0 Journal Article
%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
%V 8
%N 5
%P 748-754
%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A0748
TY - JOUR
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
Abstract: 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.
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