PSO-RBF Neural Network PID Control Algorithm of Electric Gas Pressure Regulator
Yuanchang Zhong,
Xu Huang,
Pu Meng and
Fachuan Li
Abstract and Applied Analysis, 2014, vol. 2014, 1-7
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.
Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://downloads.hindawi.com/journals/AAA/2014/731368.pdf (application/pdf)
http://downloads.hindawi.com/journals/AAA/2014/731368.xml (text/xml)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlaaa:731368
DOI: 10.1155/2014/731368
Access Statistics for this article
More articles in Abstract and Applied Analysis from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().