DSP Based Fuzzy-Neural Speed Tracking Control of Brushless DC Motor

  • Çetin Gençer
  • Ali Saygin
  • İsmail Coşkun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3949)


In this paper, Fuzzy-Neural control architecture is applied in order to construct precise speed control of brushless DC motor (BLDC) in high performance applications. BLDC motors have been becoming popular owing to high torque density, large power to weight ratio, high efficiency, high power factor, and robustness. The proposed controller is successfully implemented in real time using a digital signal processor (DSP) board DS1104 for BLDC motor. The effectiveness of the proposed controller is verified by as well as experimental results of different dynamic operating conditions. The software of system is developed in dSPACE Control Desk.


Digital Signal Processor Fuzzy Neural Network Permanent Magnet Synchronous Motor Firing Strength BLDC Motor 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Kim, K.-H., Young, M.-J.: DSP-Based High-Speed Sensorless Control for a Brushless DC Motor Using a Link Voltage Control. In: Electric Power Compenents and Systems, vol. 30, pp. 889–906. Taylor & Francis, Abington (2002)Google Scholar
  2. Rahman, M.A., Zhow, P.: Analysis of Brushless Permanent Magnet Synchronous Motors. IEEE Transactions on Industrial Electronics 43, 256–267 (1996)CrossRefGoogle Scholar
  3. Lin, F.J., Wai, R.J., Chen, H.P.: A PM Synchronous Servo Motor Drive With an On-Line Trained Fuzzy Neural Network Controller. IEEE Transactions on Energy Conversion 13, 319–325 (1998)CrossRefGoogle Scholar
  4. Lee, C.H., Teng, C.C.: Identification and Control of Dynamic Systems Using Recurrent Fuzzy Neural Networks. IEEE Transactions on Fuzzy Systems 8, 349–366 (2000)CrossRefGoogle Scholar
  5. Ibrahim, Z., And Levi, E.: Fuzzy Logic Versus PI Speed Control in High-Performance AC Drives: A Comparison, Electric Power Compenents and Systems, vol. 31, pp. 403–422. Taylor & Francis, Abington (2003)Google Scholar
  6. Lazerini, B., Reyneri, L.M., Chiaberge, M.: A Neuro-Fuzzy Approach to Hybrid Intelligent Control. IEEE Transactions on Industry Applications 35, 413–425 (1999)CrossRefGoogle Scholar
  7. Barsoum, N.: Artificial Neuron Controller for DC Drive. IEEE Power Engineering Society Winter Meeting 1, 398–402 (2000)Google Scholar
  8. Rubaai, A., Kotaru, R., Kankam, M.D.: A Real-Time Neural Network Based Controller For Brushless DC Motor Drives. In: Industry Applications Conference, 1997. Thirty-Second IAS Annual Meeting, IAS 1997, Conference Record of the 1997 IEEE, vol. 2, pp. 828–835 (1997)Google Scholar
  9. Jang, J.-S.R., Sun, C.-T.: Neuro-fuzzy Modelling and Control. Proceeding of the IEEE 83, 378–405 (1995)CrossRefGoogle Scholar
  10. Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Prentice Hall, Englewood Cliffs (1997)Google Scholar
  11. Lin, C.T., Lee, C.S.G.: Neural Fuzzy Systems. Prentice Hall, Englewood Cliffs (1996)Google Scholar
  12. Chen, Y.C., Teng, C.C.: A Model Reference Control Structure Using A Fuzzy Neural Network. Fuzzy Sets and Systems 73, 291–312 (1995)MathSciNetCrossRefMATHGoogle Scholar
  13. DS 1104 R&D Controller Board Features, dSPACE GmbH, Germany (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Çetin Gençer
    • 1
  • Ali Saygin
    • 2
  • İsmail Coşkun
    • 2
  1. 1.Fırat University Faculty of Technical EducationElaziğ
  2. 2.Gazi University Faculty of Technical EducationTeknikokullar, Ankara

Personalised recommendations