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基于在线系统辨识的DMFC模型预测与模糊控制

DMFC Model Predicted Test and Fuzzy Control Based on On-line System Recognition
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摘要 利用神经网络具有逼近任意复杂非线性函数的能力 ,以甲醇浓度为神经网络辨识模型的输入量 ,电池电压 /电流密度为输出量 ,利用 10 0 0组实验数据 ,建立了DMFC电堆的神经网络模型。然后 ,基于电特性的输入输出关系设计了一个模糊控制器 ,且利用模糊控制器的输入输出样本训练神经网络。仿真结果表明 ,所设计的神经网络模糊控制器具有自学习、自适应等优点 ,达到了在线控制的目的。 Neural network is able approaching to any complicated non-linear function. By using this feature of neural network, neural network model of DMFC electric pile is established based on 1000 sets of experimental data with the concentration of methanol as input of neural network recognition and cell voltage/current density as output. Then a fuzzy controller is designed based on relationship of input vs. output of electric characteristics. The neural network is trained from the samples of input and output of the fuzzy controller. The result of simulation shows that this neural network fuzzy controller features self-learning and self-adaptive capabilities, and the purpose of on-line control is accomplished.
出处 《自动化仪表》 CAS 北大核心 2004年第7期46-49,共4页 Process Automation Instrumentation
基金 国家 8 63计划资助项目 (编号 :2 0 0 2AA51 70 2 0 )
关键词 模糊控制器 神经网络辨识 模型预测 仿真结果 辨识 自学习 自适应 DMFC 电堆 在线系统 Neural network recognition Fuzzy controller Direct methanol fuel cell
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参考文献9

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