文献类型：期刊文献

中文题名：Output Tracking Control of a Hydrogen-air PEM Fuel Cell

英文题名：Output Tracking Control of a Hydrogen-air PEM Fuel Cell

作者：Shiwen Tong[1];Jianjun Fang[1];Yinong Zhang[1]

机构：[1]College of Automation,Beijing Union University,Beijing 100101,China

第一机构：北京联合大学城市轨道交通与物流学院

年份：2017

卷号：0

期号：2

起止页码：273-279

中文期刊名：自动化学报：英文版

收录：CSTPCD;;CSCD:【CSCD2017_2018】；

基金：supported by the Science and Technology Program of Beijing Municipal Education Commission(KM201611417009);the Project of Beijing Municipal Natural Science Foundation(4142018);the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions(CIT&TCD20150314)

语种：英文

中文关键词：Fuel cell;fuzzy cluster modeling;integral compensation;sliding mode control

外文关键词：Controllers;Fuel cells;Fuel systems;Fuzzy clustering;Navigation;Sliding mode control

摘要：Hydrogen-air proton exchange membrane(PEM)fuel cell is a promising clean energy. However, the stack output tracking control is still a challenging problem due to the soft characteristic of the stack. Both over-and less-control will cause the stack flooding or oxygen lacking which dramatically decreases the life of stacks. Traditional control methods rely on the accurate model of the fuel cell system, which is a high-order nonlinear system, and involve a complex controller design process. This paper combines the data-based fuzzy cluster modeling technology with the sliding mode control and the integral actions. The sliding mode controller tracks the dynamic changes of the fuel cell system and the integral controller eliminates the steady-state errors. Simulation results demonstrate good performance of the proposed control method.
Hydrogen-air proton exchange membrane U+0028 PEM U+0029 fuel cell is a promising clean energy. However, the stack output tracking control is still a challenging problem due to the soft characteristic of the stack. Both over- and less-control will cause the stack flooding or oxygen lacking which dramatically decreases the life of stacks. Traditional control methods rely on the accurate model of the fuel cell system, which is a high-order nonlinear system, and involve a complex controller design process. This paper combines the data-based fuzzy cluster modeling technology with the sliding mode control and the integral actions. The sliding mode controller tracks the dynamic changes of the fuel cell system and the integral controller eliminates the steady-state errors. Simulation results demonstrate good performance of the proposed control method. ? 2017 Chinese Association of Automation.