Neuro-PID Position Controller Design for Permanent Magnet Synchronous Motor

  • Mehmet Zeki Bilgin
  • Bekir Çakir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


A new speed control strategy is presented for high performance control of a Permanent Magnet Synchronous Motor (PMSM). A self-tuning Neuro-PID controller is developed for speed control. The PID gains are tuned automatically by the neural network in an on-line way. In recent years, the researches on the control of electrical machines based on ANN are increased. ANN’s, developed controller in this work, offer inherent advantages over conventional PID controller for PMSM , namely: Reduction of the effects of motor parameter variations, improvement of controller time response and improvement of drive robustness. The PMSM drive system was simulated by using MATLAB 5.0/Simulink software package. The performance of the proposed method is compared with the conventional PID methods. At the result, the control based on self-tuning Neuro-PID control has better performance than the conventional PID controller.


Permanent Magnet Synchronous Machine Motor Drive System Speed Control Strategy Permanent Magnet Synchronous Motor Drive Synchronous Motor Drive 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bose, B.K.: Technology trends in microcomputer control of electrical machines. IEEE Trans. on Ind. Electron. 35 (February 1988)Google Scholar
  2. 2.
    Sen, P.C.: Electric motor drives and control-Past, present, and future. IEEE Trans. on Ind. Electron. 37 (December 1990)Google Scholar
  3. 3.
    Bose, B.K.: Power electronics-A technology rewiev. Proc. IEEE 80 (August 1992)Google Scholar
  4. 4.
    Chan, C.C., Chau, K.T., Jiang, J.Z., Xia, W., Zhu, M., Zhang, R.: Novel Permanent Magnet Motor Drives for Electric Vehicle. IEEE Trans. on Ind. Elect. 43(2) (April 1996)Google Scholar
  5. 5.
    Hemati, N., Thorp, J.S., Leu, M.C.: Robust nonlineer control of brushless dc motors for direct-drive robotic applications. IEEE Trans. on Ind. Electron. 37 (December 1990)Google Scholar
  6. 6.
    Namdam, P.K., Sen, P.C.: A comparative study of a Lunberg observer and adaptive observer based variable structure speed control system using self controlled synchronous motor. IEEE Trans. on Ind. Electron. 28 (April 1990)Google Scholar
  7. 7.
    Matsui, N., Ohashi, H.: DSP based adaptive control of a brushless motor. IEEE Trans. on Ind. Applicat. 28 (March/April 1992)Google Scholar
  8. 8.
    Cerruto, E., Consili, A., Raciti, A., Testa, A.: A robust adaptive controller for PM motor driver in robotic application. IEEE Trans. on Power Electron. 10 (January 1995)Google Scholar
  9. 9.
    Fukuda, T., Shibata, T.: Theory and Application of Neural Networks for Industrial Control System. IEEE Trans. on Ind. Elec. 39(6) (December 1992)Google Scholar
  10. 10.
    Weerasooriya, S., El-Sharkawi, M.A.: Identification and Control of a DC Motor using Back-Propagation Neural Networks. IEEE Trans. On Energy Conversion 6(4) (September 1991)Google Scholar
  11. 11.
    Ba-Razzouk, A., Cheriti, A., Sicard, P.: Field Oriented Control of Induction Motors Using Neural-Networks Decouplers. IEEE Trans. on Power El. 12(4) (July 1997)Google Scholar
  12. 12.
    Wishart, M.T., Karley, R.G.: Identification and Control of Induction Machines Using Artificial Neural Networks. IEEE Tran. on Ind. App. 31(3) (May/June 1995)Google Scholar
  13. 13.
    Lajoe-Mazenc, M., Villanueva, C., Hector, J.: Study and Implementation of Hysteresis Controlled Inverter on a Permanent Magnet Synchronous Machine. IEEE Tran. on Ind. App. IA-21(2) (March/April 1985)Google Scholar
  14. 14.
    Omatu, S., Khalid, M., Yusof, R.: Neuro-Control and Its Applications. Springer, Berlin, Heidelberg, New York (1996)Google Scholar
  15. 15.
    Le-Huy, H., Dessaint, L.A.: An Adaptive Current Scheme for PWM Synchronous Motor Drive: Analysis and Simulation. IEEE Tran. on Power Elect. 4(4) (October 1989)Google Scholar
  16. 16.
    Ong, C.M.: Dynamic Simulation of Electric Machinery. Prentice-Hall, Englewood Cliffs (1998)Google Scholar
  17. 17.
    Narenda, K.S.: Identification and Control of Dynamical System Using Neural networks. IEEE Trans. On Energy Conversion 6(4) (September 1991)Google Scholar
  18. 18.
    Abulafya, N.: Neural Networks for System Identification and Control. Ph.D. Thesis, Imperial College of Science, Technology and Medicine, University of London (September 1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mehmet Zeki Bilgin
    • 1
  • Bekir Çakir
    • 1
  1. 1.Department of Electrical EngineeringKocaeli UniversityKocaeliTurkey

Personalised recommendations