|Table of Contents|

Temperature Modeling Based on ANFIS and Improved Fuzzy Control of DMFC


Research Field:
Publishing date:


Temperature Modeling Based on ANFIS and Improved Fuzzy Control of DMFC
QI Zhi-dong
School of Automation,NUST,Nanjing 210094,China
direct methanol fuel cell adaptive neural fuzzy inference system(ANFIS) fuzzy genetic algorithms(FGA)
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.


[1] Ren X, Zelenay P, Thomas S, et al. Recent advances in direct methanol fuel cells at Los Alamos National Laboratory[ J ]. J Power Sources, 2000, 86 ( 1 /2 ) : 111 - 116.
[2] 衣宝廉. 燃料电池———原理·技术·应用[M ]. 北 京:化学工业出版社, 2003.
[3] Costamagna P. Transport phenomena in polymericmem2 brane fuel cell [ J ]. Chemical Engineering Science, 2001, 56 (4) : 323 - 332.
[4] Rowe A, L i Xianguo. Mathematicalmodeling of p roton membrane fuel cells [ J ]. Power Source, 2001, 102 (1 /2) : 82 - 96.
[5] L in F, Wai R, Duan R. Fuzzy neural networks for i2 dentification and control of ultrasonic motor drive with LLCC resonant technique[ J ]. IEEE Trans on Industri2 al Electronics, 1999, 46 (5) : 1 331 - 1 342.
[6] Takagi T, Sugeno M. Fuzzy identification of systems and its app lication to modeling and control[ J ]. IEEE Trans on Systems Man and Cybern, 1985, 15 ( 1 ) : 116 - 132.
[7] ZhuW, L i X, Mao H, et al. Research on integrated op timal design of fuzzy controller using genetic algo2 rithms[ J ]. Computer Engineering and App lications, 2002, 23 (1) : 68 - 70, 93.
[8] Qi Z, Zhu X, Zhu W. Imp roved FGA based on the op timization of fuzzy rules [ J ]. Mini2Micro Systems, 2005, 26 (1) : 46 - 49.


Last Update: 2008-12-30