• DocumentCode
    2419601
  • Title

    Optimal tuning of tri-loop dynamic error driven controller for industrial PMDC motor drives based on particle swarm optimization-PSO

  • Author

    Sharaf, Adel M. ; El-Gammal, Adel A A

  • Author_Institution
    Centre for Energy Syst., Univ. of Trinidad & Tobago UTT, Trinidad and Tobago
  • fYear
    2009
  • fDate
    17-20 May 2009
  • Firstpage
    470
  • Lastpage
    476
  • Abstract
    The paper presents the novel application of Particle Swarm optimization PSO for the optimal tuning of a driven error controller for high performance permanent magnet PMDC industrial motor drives. PSO searching algorithm is implemented for tuning the proposed dynamic tri-loop controller gains: the motor speed error (eomega), the armature current deviation (eI) from its maximum or ldquospecifiedrdquo allowable current level and dynamic current ripple error (eR) that define the total error function (et), the integral of the total error and the integral of the total error square. The control voltage signal is used to regulate the firing delay angle alpha of the 3-phase controlled rectifier bridge.
  • Keywords
    industrial control; motor drives; particle swarm optimisation; permanent magnet motors; three-term control; 3-phase controlled rectifier bridge; armature current deviation; control voltage signal; dynamic current ripple error; firing delay angle; industrial PMDC motor drives; motor speed error; optimal tuning; particle swarm optimization; permanent magnet motor drives; total error function; tri-loop dynamic error driven controller; Bridge circuits; Delay; Error correction; Industrial control; Motor drives; Optimal control; Particle swarm optimization; Permanent magnet motors; Rectifiers; Voltage control; PMDC Motor Drive; Particle Swarm Optimization PSO; Tuning controller gains; total error minimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Electronics and Motion Control Conference, 2009. IPEMC '09. IEEE 6th International
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-3556-2
  • Electronic_ISBN
    978-1-4244-3557-9
  • Type

    conf

  • DOI
    10.1109/IPEMC.2009.5157433
  • Filename
    5157433