• DocumentCode
    3221784
  • Title

    Minimal overshoot direct torque control for permanent magnet synchronous motors using Hybrid Bacteria Foraging-Particle Swarm Optimization

  • Author

    Bayoumi, Ehab H. E.

  • Author_Institution
    Dept. of Electr. & Electron. Eng. Technol., Abu Dhabi Men´s Coll. (ADMC), Abu Dhabi, United Arab Emirates
  • fYear
    2013
  • fDate
    16-19 April 2013
  • Firstpage
    112
  • Lastpage
    119
  • Abstract
    This paper presents a robust torque, flux and speed controller´s design method for permanent magnet synchronous motor (PMSM) drive using Direct Torque Control (DTC). A simple algorithm is presented to adjust the parameters of torque, flux and speed controllers. This mini-max optimization problem is solved using Hybrid Bacteria Foraging-Particle Swarm Optimization Approach (BF-PSO). The solution thus obtained is global optimal and robust. The proposed technique eliminates common problems including; torque ripples, low speed and integration drift. As well as it characterized by fast tracking capability, minimal overshoot responses, and robust to load disturbances and low speed operation. Results prove the effectiveness and viability of the proposed technique.
  • Keywords
    machine control; minimax techniques; particle swarm optimisation; permanent magnet motors; robust control; synchronous motor drives; torque control; velocity control; DTC; PMSM drive; flux controller; hBF-PSO; hybrid bacteria foraging-particle swarm optimization; load disturbances; mini-max optimization problem; overshoot direct torque control; overshoot responses; permanent magnet synchronous motor drive; robust torque controller; speed controller design method; torque ripples; tracking capability; Couplings; Inverters; Microorganisms; Optimization; Stators; Torque; Vectors; PI controller; PMSM drive; bacteria foraging optimization; direct torque control; minimal overshoot; particle swarm optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence in Control and Automation (CICA), 2013 IEEE Symposium on
  • Conference_Location
    Singapore
  • Type

    conf

  • DOI
    10.1109/CICA.2013.6611671
  • Filename
    6611671