On-line tuning PID parameters in an idling engine based on a modified BP neural network by particle swarm optimization

Jia Meng Yin*, Ji Sun Shin, Hee Hyol Lee

*この研究の対応する著者

研究成果査読

14 被引用数 (Scopus)

抄録

PID control systems are widely used in many fields, and many methods to tune the parameters of PID controllers are known. When the characteristics of the object are changed, the traditional PID control should be adjusted by empirical knowledge. This may result in a worse performance by the system. In this article, a new method to tune PID parameters, called the back-propagation network modified by particle swarm optimization, is proposed. This algorithm combines conventional PID control with a back propagation neural network (BPNN) and particle swarm optimization (PSO). This method is demonstrated in the engine idling-speed control problem. The proposed method provides considerable performance benefits compared with a traditional controller in this simulation.

本文言語English
ページ(範囲)129-133
ページ数5
ジャーナルArtificial Life and Robotics
14
2
DOI
出版ステータスPublished - 2009 11 1

ASJC Scopus subject areas

  • 生化学、遺伝学、分子生物学(全般)
  • 人工知能

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