基于系统辨识的燃料电池系统建模和自适应模糊控制

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

熔融碳酸盐燃料电池 (MCFC)发电运行时 ,电堆的工作温度必须控制在一定的范围内 ,否则将导致系统发电效率的降低或危及电堆寿命 .因此 ,实现对 MCFC运行温度的在线控制势在必行 .但由于 MCFC系统的复杂性 ,已有模型均为复杂的非线性微分方程组描述的解析模型 ,难以满足在线计算的实时控制的要求 .因此 ,本文首先利用神经网络辨识技术基于实验的输入 (气体流量 )输出 (温度 )数据建立起 MCFC电堆的神经网络模型 ;然后 ,基于这一电堆模型 ,设计了一个 MCFC电堆工作温度的在线改进型自适应模糊控制器 .该控制器对传统的模糊控制方法存在的缺陷进行了改进 ,它一方面采用 BP算法对模糊系统的参数进行修正 ,另一方面又通过聚类算法对模糊系统的结构进行自适应调整 .最后 ,用神经网络辨识模型代替实际的MCFC电堆进行了控制仿真 ,仿真结果证明对 MCFC辨识电堆建模的有效性 。

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.