PSO-RBF Neural Network PID Control Algorithm of Electric Gas Pressure Regulator

被引：4
|
作者：
Zhong, Yuanchang [1 ,2 ]
Huang, Xu [1 ]
Meng, Pu [1 ]
Li, Fachuan [1 ]
机构：
[1] Chongqing Univ, Coll Commun Engn, Chongqing 400044, Peoples R China
[2] Chongqing Univ, Sch Automat, Chongqing 400044, Peoples R China
来源：
ABSTRACT AND APPLIED ANALYSIS | 2014年
基金：
中国国家自然科学基金;
关键词：
D O I：
10.1155/2014/731368
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The current electric gas pressure regulator often adopts the conventional PID control algorithm to take drive control of the core part (micromotor) of electric gas pressure regulator. In order to further improve tracking performance and to shorten response time, this paper presents an improved PID intelligent control algorithm which applies to the electric gas pressure regulator. The algorithm uses the improved RBF neural network based on PSO algorithm to make online adjustment on PID parameters. Theoretical analysis and simulation result show that the algorithm shortens the step response time and improves tracking performance.
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收藏
页数：7
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