引用本文:戚志东,朱新坚.基于一种FGA–ANFIS技术的DMFC温度建模和控制[J].控制理论与应用,2008,25(4):738~742.[点击复制]
QI Zhi-dong,ZHU Xin-jian.Temperature modeling and control of DMFC based on FGA–ANFIS technology[J].Control Theory and Technology,2008,25(4):738~742.[点击复制]
基于一种FGA–ANFIS技术的DMFC温度建模和控制
Temperature modeling and control of DMFC based on FGA–ANFIS technology
摘要点击 1119  全文点击 906  投稿时间：2005-09-23  修订日期：2007-05-09
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DOI编号  10.7641/j.issn.1000-8152.2008.4.028
  2008,25(4):738-742
中文关键词  直接甲醇燃料电池  自适应神经模糊推理系统  模糊遗传算法
英文关键词  direct methanol fuel cell  adaptive neural fuzzy inference system  fuzzy genetic algorithms
基金项目  国家863项目资助(2002AA517020).
作者单位E-mail
戚志东 南京理工大学 自动化学院, 江苏 南京 210094 qizhidong@sina.com.cn 
朱新坚 上海交通大学 燃料电池研究所, 上海 200030 xjzhu@sjtu.edu.cn 
中文摘要
      为了提高直接甲醇燃料电池(DMFC)的发电性能, 采用自适应神经模糊推理技术(FGA–ANFIS)对电池的工作温度进行建模与控制. 首先, 基于实验的输入输出数据建立了DMFC电堆温度的自适应神经模糊辨识模型, 避开了DMFC电堆的内部复杂性. 然后, 将训练好的网络模型作为DMFC控制系统的参考模型, 采用一种改进的模糊遗传算法对神经模糊控制器的参数和模糊规则进行自适应调整. 最后, 通过仿真, 将所提出的算法与非线性PID和传统模糊算法进行比较, 结果表明所设计的神经模糊控制器具有较好的性能
英文摘要
      To improve the performance of a direct methanol fuel cell (DMFC), the adaptive neural fuzzy inference (FGA–ANFIS) technology is applied to the modeling and control of a DMFC temperature system. First, an adaptive neural fuzzy inference system (ANFIS) identification model of DMFC stack temperature is developed based on the input-output sampled data, getting around the internal complexity of DMFC stack. Then, taking the well-trained network model as the reference model of the control system of DMFC stack, we use a novel fuzzy genetic algorithm (FGA) for adaptively adjusting the parameters and fuzzy rules of a neural fuzzy controller. Simulation results demonstrate better performance of this neural fuzzy controller in comparison with those of the nonlinear PID and traditional fuzzy algorithm.