| Application of Nonlinear Predictive Control Based on RBF Network Predictive Model in MCFC Plant |
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| 作者姓名： | 陈跃华 曹广益 朱新坚 |
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| 作者单位： | Institute of Fuel Cell Shanghai Jiaotong Univ.，Institute of Fuel Cell，Shanghai Jiaotong Univ.，Institute of Fuel Cell，Shanghai Jiaotong Univ.，Shanghai 200030 China，Shanghai 200030 China，Shanghai 200030 China |
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| 基金项目： | 国家高技术研究发展计划(863计划) |
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| 摘 要： | This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.
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| 关 键 词： | 非线性模型控制 碳酸盐燃料信元 神经中枢网络模式 应用方法 |
| 文章编号： | 1007-1172（2007）01-0042-05 |
| 修稿时间： | 2006-06-23 |
Application of Nonlinear Predictive Control Based on RBF Network Predictive Model in MCFC Plant |
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| CHEN Yue-hua,CAO Guang-yi,ZHU Xin-jian.Application of Nonlinear Predictive Control Based on RBF Network Predictive Model in MCFC Plant[J].Journal of Shanghai Jiaotong university,2007,12(1):42-46,52. |
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| Authors: | CHEN Yue-hua CAO Guang-yi ZHU Xin-jian |
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| Institution: | Institute of Fuel Cell, Shanghai Jiaotong Univ. , Shanghai 200030, China |
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| Abstract: | This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely. |
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| Keywords: | molten carbonate fuel cell (MCFC) radial basis function (RBF) neural network model nonlinear model predictive control (NMPC) golden mean method |
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