Speed Control of Permanent Magnet Synchronous Motor Using FOC Neural Network

  • Nooradzianie Muhd. Zin
  • Wahyu Mulyo Utomo
  • Zainal Alam Haron
  • Azuwien Aida Bohari
  • Sy Yi Sim
  • Roslina Mat Ariff
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 253)

Abstract

This paper presents the performance analysis of the field oriented control for a permanent magnet synchronous motor drive with a proportional-integral-derivative and artificial neural network controller in closed loop operation. The mathematical model of permanent magnet synchronous motor and artificial neural network algorithm is derived. While, the current controlled voltage source inverter feeding power to the motor is powered from space vector pulse width modulation current controlled converter. The effectiveness of the proposed method is verified by develop simulation model in MATLAB-Simulink program. The simulation results prove the proposed artificial neural network controller produce significant improvement control performance compare to the proportional-integral-derivative controller for both condition controlling speed reference variations and constant load. It can conclude that by using proposed controller, the overshoot, steady state error and rise time can be reducing significantly.

Keywords:

Permanent magnet synchronous motor drive Field oriented control Artificial neural network 

Notes

Acknowledgments

All the authors would like to express a sincere acknowledgments to Universiti Tun Hussein Onn Malaysia for the valuable support during completion this research and manuscript.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Nooradzianie Muhd. Zin
    • 1
  • Wahyu Mulyo Utomo
    • 1
  • Zainal Alam Haron
    • 1
  • Azuwien Aida Bohari
    • 1
  • Sy Yi Sim
    • 1
  • Roslina Mat Ariff
    • 2
  1. 1.Electrical Power Department, Faculty of Electrical and Electronic EngineeringUniversiti Tun Hussein Onn MalaysiaJohorMalaysia
  2. 2.Robotic and Mechatronic Department, Faculty of Electrical and Electronic EngineeringUniversiti Tun Hussein Onn MalaysiaJohorMalaysia

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