摘要
针对直接甲醇燃料电池(DMFC)的实时控制要求,采用自适应神经模糊推理系统(AN-FIS)对DMFC系统的工作温度进行建模与控制。基于实验数据建立DMFC电堆温度模型,避免了DMFC电堆的内部复杂性分析。以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(FGA)在线对神经模糊控制器的参数和模糊规则进行自适应调整。将所提出的算法与非线性PID和传统模糊算法进行实验比较,结果表明所设计的神经模糊控制器具有较好的性能。
To improve the performance of direct methanol fuel cell （ DMFC ）, an adaptive neural fuzzy inference technology is adopted in the modeling and control of DMFC temperature system. In the modeling process, an ANFIS identification model of DMFC stack temperature is developed based on the input-output sampled data, which avoids the internal complexity of DMFC stack. In the controlling process, with the network model trained well as the reference model of the control system of DMFC stack, a novel fuzzy genetic algorithm （FGA） is used to regulate the parameters and fuzzy rules of a neural fuzzy controller. In the simulation, compared with the nonlinear proportional integral derivative （PID） and traditional fuzzy algorithms, the neural fuzzy controller designed in this paper gets better performance, as demonstrated by the simulation results.
出处
《南京理工大学学报》
EI
CAS
CSCD
北大核心
2008年第6期749-753,共5页
Journal of Nanjing University of Science and Technology
基金
国家"863"计划(2002AA517020)
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
direct methanol fuel cell
adaptive neural fuzzy inference system （ANFIS）
fuzzy genetic algorithms （FGA）
