Knowledge-based Particle Swarm Optimization for PID Controller Tuning

Junfeng Chen, Mohammad Nabi Omidvar, Morteza Azad, Xin Yao

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

13 Citations (Scopus)
317 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationProceedings of the IEEE Congress on Evolutionary Computation 2017
PublisherIEEE Computer Society Press
Number of pages8
ISBN (Electronic)978-1-5090-4601-0
ISBN (Print)978-1-5090-4602-7
Publication statusPublished - 7 Jul 2017
EventIEEE Congress on Evolutionary Computation 2017 - Donostia-San Sebastian, Spain
Duration: 5 Jun 20178 Jun 2017


ConferenceIEEE Congress on Evolutionary Computation 2017
Abbreviated titleCEC 2017
CityDonostia-San Sebastian


Dive into the research topics of 'Knowledge-based Particle Swarm Optimization for PID Controller Tuning'. Together they form a unique fingerprint.

Cite this