Full Text:   <1559>

CLC number: TP273; TM911.4

On-line Access: 

Received: 2007-07-30

Revision Accepted: 2007-10-08

Crosschecked: 0000-00-00

Cited: 1

Clicked: 3461

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.12 P.1921~1927


Iterative learning control of SOFC based on ARX identification model

Author(s):  HUO Hai-bo, ZHU Xin-jian, TU Heng-yong

Affiliation(s):  Fuel Cell Research Institute, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China

Corresponding email(s):   huohb1028@sjtu.edu.cn

Key Words:  Autoregressive model with exogenous input (ARX), Iterative learning control (ILC), Solid oxide fuel cell (SOFC), Identification

HUO Hai-bo, ZHU Xin-jian, TU Heng-yong. Iterative learning control of SOFC based on ARX identification model[J]. Journal of Zhejiang University Science A, 2007, 8(12): 1921~1927.

@article{title="Iterative learning control of SOFC based on ARX identification model",
author="HUO Hai-bo, ZHU Xin-jian, TU Heng-yong",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Iterative learning control of SOFC based on ARX identification model
%A HUO Hai-bo
%A ZHU Xin-jian
%A TU Heng-yong
%J Journal of Zhejiang University SCIENCE A
%V 8
%N 12
%P 1921~1927
%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A1921

T1 - Iterative learning control of SOFC based on ARX identification model
A1 - HUO Hai-bo
A1 - ZHU Xin-jian
A1 - TU Heng-yong
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 12
SP - 1921
EP - 1927
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A1921

This paper presents an application of iterative learning control (ILC) technique to the voltage control of solid oxide fuel cell (SOFC) stack. To meet the demands of the control system design, an autoregressive model with exogenous input (ARX) is established. Firstly, by regulating the variation of the hydrogen flow rate proportional to that of the current, the fuel utilization of the SOFC is kept within its admissible range. Then, based on the ARX model, three kinds of ILC controllers, i.e. P-, PI- and PD-type are designed to keep the voltage at a desired level. Simulation results demonstrate the potential of the ARX model applied to the control of the SOFC, and prove the excellence of the ILC controllers for the voltage control of the SOFC.

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


[1] Arimoto, S., Kawamura, S., Miyazaki, F., 1984. Bettering Operation of Dynamic Systems by Learning: A New Control Theory for Servomechanism or Mechatronics Systems. Proc. IEEE Conf. on Decision and Control including Symp. on Adaptive Processes. Las Vegas, p.1064-1069.

[2] Chow, T.W.S., Li, X.D., Fang, Y., 2000. A real-time learning control approach for nonlinear continuous-time system using recurrent neural networks. IEEE Trans. on Ind. Electr., 47(2):478-486.

[3] Fang, Y., Chow, T.W.S., 1998. Iterative learning control of linear discrete-time multivariable systems. Automatica, 34(11):1459-1462.

[4] Fang, X., Chen, P., Shao, J., 2005. Optimal Higher-order Iterative Learning Control of Discrete-time Linear Systems. Proc. IEE Conf. on Control Theory and Applications. United Kingdom, p.43-48.

[5] Ghaffari, A., Roshanian, J., Tayefi, M., 2007. Time-varying transfer function extraction of an unstable launch vehicle via closed-loop identification. Aerosp. Sci. Technol., 11(2):238-244.

[6] Gopinath, S., Kar, I.N., 2004. Iterative learning control scheme for manipulators including actuator dynamics. Mechanism and Machine Theory, 39(12):1367-1384.

[7] Hall, D.J., Colclaser, R.G., 1999. Transient modeling and simulation of a tubular solid oxide fuel cell. IEEE Trans. on Energy Conversion, 14(3):749-753.

[8] Jurado, F., 2004. Modeling SOFC plants on the distribution system using identification algorithms. J. Power Sources, 129(2):205-215.

[9] Jurado, F., 2006. A method for the identification of solid oxide fuel cells using a Hammerstein model. J. Power Sources, 154(1):145-152.

[10] Kandepu, R., Imsland, L., Foss, B. A., Stiller, C., Thorud, B., Bolland, O., 2007. Modeling and control of a SOFC-GT-based autonomous power system. Energy, 32(4):406-417.

[11] Li, X.D., Chow, T.W.S., Ho, J.K.L., 2005. 2-D system theory based iterative learning control for linear continuous systems with time delays. IEEE Trans. on Circuits and Systems, 52(7):1421-1430.

[12] Li, Y.H., Rajakaruna, S., Choi, S.S., 2007. Control of a solid oxide fuel cell power plant in a grid-connected system. IEEE Trans. on Energy Conversion, 22(2):405-413.

[13] Ljung, L., 1999. System Identification: Theory for the User. Prentice Hall, Upper Saddle River, NJ.

[14] Moore, K.L., Chen, Y.Q., Bahl, V., 2005. Monotonically convergent iterative learning control for linear discrete-time systems. Automatica, 41(9):1529-1537.

[15] Padullés, J., Ault, G.W., McDonald, J.R., 2000. An integrated SOFC plant dynamic model for power systems simulation. J. Power Sources, 86(1-2):495-500.

[16] Qi, Y.T., Huang, B., Chuang, K.T., 2005. Dynamic modeling of solid oxide fuel cell: the effect of diffusion and inherent impedance. J. Power Sources, 150(1-2):32-47.

[17] Sedghisigarchi, K., Feliachi, A., 2004. Dynamic and transient analysis of power distribution systems with fuel cells-part I: Fuel-cell dynamic model. IEEE Trans. on Energy Conversion, 19(2):423-428.

[18] Sedghisigarchi, K., Feliachi, A., 2006. Impact of fuel cells on load-frequency control in power distribution systems. IEEE Trans. on Energy Conversion, 21(1):250-256.

[19] Sugie, T., Ono, T., 1991. An iterative learning control law for dynamical systems. Automatica, 27(4):729-732.

[20] Udagawa, J., Aguiar, P., Brandon, N.P., 2007. Hydrogen production through steam electrolysis: model-based steady state performance of a cathode-supported intermediate temperature solid oxide electrolysis cell. J. Power Sources, 166(1):127-136.

[21] Wang, D., 1998. Convergence and robustness of discrete time nonlinear systems with iterative learning control. Automatica, 34(11):1445-1448.

[22] Zhu, Y., Tomsovic, K., 2002. Development of models for analyzing the load-following performance of microturbines and fuel cells. Electric Power Systems Research, 62(1):1-11.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE