SHEN Cheng, CAO Guang-yi, ZHU Xin-jian. MODELING BASED ON IDENTIFICATION AND AN IMPROVED ADAPTIVE FUZZY CONTROL OF MOLTEN CARBONATE FUEL CELL(MCFC)[J]. INFORMATION AND CONTROL, 2002, 31(1): 73-78.
Citation: SHEN Cheng, CAO Guang-yi, ZHU Xin-jian. MODELING BASED ON IDENTIFICATION AND AN IMPROVED ADAPTIVE FUZZY CONTROL OF MOLTEN CARBONATE FUEL CELL(MCFC)[J]. INFORMATION AND CONTROL, 2002, 31(1): 73-78.

MODELING BASED ON IDENTIFICATION AND AN IMPROVED ADAPTIVE FUZZY CONTROL OF MOLTEN CARBONATE FUEL CELL(MCFC)

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  • Received Date: April 14, 2001
  • Published Date: February 19, 2002
  • The operating temperatures in Molten Carbonate Fuel Cells (MCFC) stack must be controlled within a specific range. The efficiency of the stack will be lower and the lifespan of the stack will be shorter if operating at too high or too low temperature. So it is very important to control the temperatures in the stack. Unfortunately, for the seriously complexes of MCFC, modeling MCFC is very difficult. The models existed are all the analytical models described with complicated nonlinear differential equations and poor for online-control. So, In this paper, firstly, a neural networks identification model of MCFC stack is developed based on measured input-output sampled data. Then, an online novel adaptive fuzzy controller of MCFC stack is developed. Some drawbacks of traditional fuzzy control method are improved. On the one hand, the parameters of the fuzzy system are regulated, adopting BP algorithm, on the other hand, the rule database of fuzzy system is adaptively adjusted, adopting the nearest neighbor-clustering algorithm. In the end, using the neural networks model of MCFC stack as the real MCFC stack the control simulations is carried out. The validity of neural networks identification modeling of MCFC stack and the superior performance of the novel adaptive fuzzy controller are demonstrated by simulation.
  • [1]
    Cao Guangyi, Masumi Masubuchi, Dynamic Modeling and Response in Molten Carbonate Fuel Cell, Transactions of the Japan Society of Mechanical Engineers(JSME), 1991, 57535(B):837~842
    [2]
    R H echat-Melsen, Application of counterpropagation networks, Neural networks,1, 1988, 131~139
    [3]
    D F Specht. Probabilistic neural networks and the polynominal Adeline as complementary techniques for classification, IEEE Transactions on Neural Networks 1, 1990, 111~121
    [4]
    L Monostori, D Barschdorff. Artificial neural networks in intelligent manufacturing, Robotics and Computer Integrated Manufacturing, 1992,9:412~436
    [5]
    S A Billings, S Chen. nonlinear system identification using neural networks, International Journal of Control, 1990,51(6):1191~1214
    [6]
    Kosko B. Neural Networks and Fuzzy Systems-A Dynamical Systems Approach to Machine Intelligence, Englewood Cliffs, NJ: Prentice-Hall, 1992, P1~50
    [7]
    Ahmed Rubaai, Raj Kotaru Online Identification and Control of a DC Motor Using Learning Adaptation of Neural Networks,IEEE Trans. on Industry Applications, May/June 2000,36(3)
    [8]
    Faa-Jeng Lin, Rong-Jong Wai, Rou-Yong Duan. Fuzzy Neural Networks for Identification and Control of Ultrasonic Motor Drive with LLCC Resonant Technique, IEEE Trans. on Industrial Electronics, Oct.1999, 46(5)
    [9]
    刘耘. 神经网络与模糊控制在熔融碳酸盐燃料电池系统控制中的应用. [学位论文],上海交通大学,2000
    [10]
    王立新.自适应模糊系统与控制——设计与稳定性分析. 国防工业出版社,1995
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