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, HeeHyol Lee

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)129-133
Number of pages5
JournalArtificial Life and Robotics
Volume14
Issue number2
DOIs
Publication statusPublished - 2009 Nov

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Keywords

  • BP neural network
  • Engine idling-speed control
  • Particle swarm optimization
  • PID control

ASJC Scopus subject areas

  • Artificial Intelligence
  • Biochemistry, Genetics and Molecular Biology(all)

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