Knowledge-based Particle Swarm Optimization for PID Controller Tuning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Colleges, School and Institutes

External organisations

  • Hohai University, Changzhou, China
  • University of Birmingham
  • Southern University of Science and Technology

Abstract

A proportional-integral-derivative (PID) controller is a control loop feedback mechanism widely employed in industrial control systems. The parameters tuning is a sticking point, having a great effect on the control performance of a
PID system. There is no perfect rule for designing controllers, and finding an initial good guess for the parameters of a wellperforming controller is difficult. In this paper, we develop a knowledge-based particle swarm optimization by incorporating the dynamic response information of PID into the optimizer. Prior
knowledge not only empowers the particle swarm optimization algorithm to quickly identify the promising regions, but also helps the proposed algorithm to increase the solution precision in the limited running time. To benchmark the performance of the proposed algorithm, an electric pump drive and an automatic voltage regulator system are selected from industrial applications.
The simulation results indicate that the proposed algorithm with a newly proposed performance index has a significant performance on both test cases and outperforms other algorithms in terms of overshoot, steady state error, and settling time.

Details

Original languageEnglish
Title of host publicationProceedings of the IEEE Congress on Evolutionary Computation 2017
Publication statusPublished - 7 Jul 2017
EventIEEE Congress on Evolutionary Computation 2017 - Donostia-San Sebastian, Spain
Duration: 5 Jun 20178 Jun 2017

Conference

ConferenceIEEE Congress on Evolutionary Computation 2017
Abbreviated titleCEC 2017
CountrySpain
CityDonostia-San Sebastian
Period5/06/178/06/17