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CLC number: TP273.3; TP183

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 0000-00-00

Cited: 1

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Citations:  Bibtex RefMan EndNote GB/T7714

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

http://doi.org/10.1631/jzus.2007.A0748


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",
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pages="748-754",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A0748"
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%DOI 10.1631/jzus.2007.A0748

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T1 - LS-SVM model based nonlinear predictive control for MCFC system
A1 - CHEN Yue-hua
A1 - CAO Guang-yi
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PB - Zhejiang University Press & Springer
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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.

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

Reference

[1] Belarbi, K., Titel, F., Bourebia, W., Benmahammed, K., 2005. Design of Mamdani fuzzy logic controllers with rule base minimization using genetic algorithm. Engineering Applications of Artificial Intelligence, 18(7):875-880.

[2] Elliott, L., Ingham, D.B., Kyne, A.G., Mera, N.S., Pourkashanian, M., Wilson, C.W., 2005. The use of ignition delay time in genetic algorithms optimization of chemical kinetics reaction mechanisms. Engineering Applications of Artificial Intelligence, 18(7):825-831.

[3] Golbert, J., Lewin, D.R., 2004. Model-based control of fuel cells: (1) Regulatory control. Journal of Power Sources, 135(1-2):135-151.

[4] Ishikawa, T., Yasue, H., 2000. Start-up, testing and operation of 1000 kW class MCFC power plant. Journal of Power Sources, 86(1-2):145-150.

[5] Li, X., Cao, G.Y., Zhu, X.J., 2006. Modeling and control of PEMFC based on least squares support vector machines. Energy Conversion and Management, 47(7-8):1032-1050.

[6] Lunghi, P., Bove, R., Desideri, U., 2003. Analysis and optimization of hybrid MCFC gas turbines plants. Journal of Power Sources, 118(1-2):108-117.

[7] Pelckmans, K., Suykens, J., van Gestel, T., de Brabanter, J., Lukas, L., Hamers, B., de Moor, B., Vandewalle, J., 2003. LS-SVMlab: A MATLAB/C toolbox for Least Squares Support Vector Machines. Http://www.esat.kuleuven.ac.be/sista/lssvmlab

[8] Schumacher, J.O., Gemmar, P., Denne, M., 2004. Control of miniature proton exchange membrane fuel cells based on fuzzy logic. Journal of Power Sources, 129(2):143-151.

[9] Shen, C., Cao, G.Y., Zhu, X.J., 2002. Nonlinear modeling and adaptive fuzzy control of MCFC stack. Journal of Process Control, 12(8):831-839.

[10] Suykens, J.A.K., Vandewalle, J., 2000. Recurrent least squares support machines. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 47(7):1109-1114.

[11] Vong, C.M., Wong, P.K., Li, Y.P., 2006. Prediction of automotive engine power and torque using least squares support machines and Bayesian inference. Engineering Applications of Artificial Intelligence, 19(3):277-287.

[12] Wang, S.W., Yu, D.L., Gomm, J.B., Page, G.F., Douglas, S.S., 2006. Adaptive neural network model based predictive control for air-fuel ratio of SI engines. Engineering Applications of Artificial Intelligence, 19(2):189-200.

[13] Yoshiba, F., Izaki, Y., Watanabe, T., 2004. Wide range load controllable MCFC cycle with pressure swing operation. Journal of Power Sources, 137(2):196-205.

[14] Zhu, J., 2002. Intelligent Predictive Control Technology and Application. Zhejiang University Press, Hangzhou, China, p.138-140 (in Chinese).

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