Knowledge-based particle swarm optimization for PID controller tuning

Junfeng CHEN, Mohammad Nabi OMIDVAR, Morteza AZAD, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

23 Citations (Scopus)


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 well-performing 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. © 2017 IEEE.
Original languageEnglish
Title of host publication2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Print)9781509046010
Publication statusPublished - Jun 2017
Externally publishedYes

Bibliographical note

This work was partially supported by National Natural Science Foundation of China (grant no. 61403121), Fundamental Research Funds for the Central Universities (grant no. 2015B20214), and EPSRC (grant nos. EP/K001523/1 and EP/J017515/1). Xin Yao was supported by a Royal Society Wolfson Research Merit Award.


  • Knowledge
  • Particle Swarm Optimization
  • PID Controller


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