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Abstract and Applied Analysis
Volume 2014, Article ID 731368, 7 pages
http://dx.doi.org/10.1155/2014/731368
Research Article

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

1College of Communication Engineering, Chongqing University, Chongqing 400044, China
2School of Automation, Chongqing University, Chongqing 400044, China

Received 8 April 2014; Accepted 2 June 2014; Published 24 July 2014

Academic Editor: Zidong Wang

Copyright © 2014 Yuanchang Zhong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.