Parameter Space Design of Speed Controller for BLDC Motor Using SIWPSO-RBFNN Algorithm

Ya Wen Hsu, Guan Yan Chen, Jau Woei Perng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this study, an intelligent method is used at the optimal parameters of a time delay based proportional integral (PI) controller for brushless direct current (BLDC) motor speed control system problem. The intelligent method uses the particle swarm optimization (PSO) method to search the optimal proportional gain and the integral gain of the PI control parameter space when the delay time varies. Then, a radial basis function neural network (RBFNN) method is used to obtain an optimal parametric fitting curve from the results of the stochastic inertia weight in PSO (SIWPSO) method. The 3D stability boundaries is shown in graphical form in the control parameter space. The optimal PI control parameters for the speed control are illustrated in a BLDC motor with a digital signal processor (DSP) based control platform. Details of the simulated results and DSP-based experimental results are also presented in this study.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2146-2151
Number of pages6
ISBN (Electronic)9781538666500
DOIs
StatePublished - 16 Jan 2019
Event2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan
Duration: 7 Oct 201810 Oct 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Country/TerritoryJapan
CityMiyazaki
Period7/10/1810/10/18

Keywords

  • Brushless DC motor
  • Particle swarm optimization
  • Radial basis function neural network
  • Time delay

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