On-line Optimization of Fuzzy-Immune PID for PEMFC Temperature Control Based on RBF Neural Network

被引：0
|
作者：
Li, Dazi [1 ]
Yu, Yadi [1 ]
Jin, Qibing [1 ]
机构：
[1] Beijing Univ Chem Technol, Inst Automat, Coll Informat Sci & Technol, 15 North East Rd Sanhuan, Beijing 10029, Peoples R China
来源：
PROCEEDINGS OF 2013 CHINESE INTELLIGENT AUTOMATION CONFERENCE: INTELLIGENT AUTOMATION | 2013年 / 254卷
关键词：
PEMFC; Fuzzy-immune PID; RBF neural network; Temperature control; SYSTEM;
D O I：
10.1007/978-3-642-38524-7_23
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Proton Exchange Membrane Fuel Cell (PEMFC) temperature exists complex nonlinearity and is deeply disturbed by load change. Considering the characteristics of PEMFC temperature control, an improved fuzzy-immune PID algorithm is derived based on the immune feedback regulating law. Compared with general fuzzy-immune PID algorithm, radial basis function (RBF) neural network is introduced to the on-line optimization work of fuzzy-immune PID parameters, which optimizes the PID parameters on-line. Simulation results show that the proposed method in this study achieves good performance in temperature control and is useful for wide application of PEMFC.
引用
收藏
页码：215 / 222
页数：8
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