A Hybrid Neuro-Fuzzy Controller for Brushless DC Motors

  • Muammer Gökbulut
  • Beşir Dandil
  • Cafer Bal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3949)


In this paper, a hybrid neuro-fuzzy controller (NFC) is presented for the speed control of brushless DC motors to improve the control performance of the drive under transient and steady state conditions. In the hybrid control system, proportional-derivative (PD) type neuro-fuzzy controller (NFC) is the main tracking controller, and an integral compensator is proposed to compensate the steady state errors. A simple and smooth activation mechanism described for integral compensator modifies the control law adaptively. The presented BLDC drive has fast tracking capability, less steady state error and robust to load disturbance, and do not need complicated control method. Experimental results showing the effectiveness of the proposed control system are presented.


Fuzzy Neural Network Steady State Error BLDC Motor Direct Torque Control Propose Control System 
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.
    Rahman, M.A., Zhow, P.: Analysis of brushless permanent magnet synchronous motors. IEEE Transactions on Industrial Electronics 43, 256–267 (1996)CrossRefGoogle Scholar
  2. 2.
    Boldea, I., Nasar, S.A.: Vector Control of AC Drives. CRC Pres, New York (1992)Google Scholar
  3. 3.
    Gokbulut, M.: Adaptive control of brushless DC motors using neural networks, Ph.D Thesis, Erciyes University Kayseri (1998)Google Scholar
  4. 4.
    El-Sharkawi, M.A.: Development and implementation of high performance variable structure tracking control for brushless motors. IEEE Trans. on Energy Conversion 6, 114–119 (1991)CrossRefGoogle Scholar
  5. 5.
    El-Samahy, A.A., El-Sharkawi, M.A., Sharaf, S.M.: Adaptive multi-layer self-tuning tracking control for DC brushless motors. IEEE Trans. on Energy Conversion 9, 311–316 (1994)CrossRefGoogle Scholar
  6. 6.
    Da, F., Song, W.: Fuzzy neural networks for direct adaptive control. IEEE Transactions on Industrial Electronics 50, 507–513 (2003)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    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
  9. 9.
    Dandil, B.: Robust speed control of induction motors using neuro-fuzzy controllers, Ph.D Thesis. Firat University Elazig (2004)Google Scholar
  10. 10.
    Rubai, A., Ricketts, D., Kanham, D.: Development and implementation of an adaptive fuzzy-neural network controller for brushless drivers. IEEE Transaction on Industry Applications 38, 441–447 (2002)CrossRefGoogle Scholar
  11. 11.
    Wai, R.J., Lin, F.J.: Fuzzy neural network sliding mode position controller for induction motor drive. IEE Proc. Electrical Power Appl. 146, 297–308 (1999)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Er, M.J., Gao, Y.: Robust adaptive control of robot manipulators using generalized fuzzy neural networks. IEEE Transactions on Industrial Electronics 50, 620–628 (2003)CrossRefGoogle Scholar
  14. 14.
    Lin, F.J., Wai, R.J.: Adaptive fuzzy-neural network control for induction spindle motor drive. IEEE Trans. on Energy Conversion 17, 507–513 (2002)CrossRefGoogle Scholar
  15. 15.
    Lee, C.H., Teng, C.C.: Identification and control of dynamic systems using recurrent fuzzy neural Networks. IEEE Transactions on Fuzzy System 8, 349–366 (2000)CrossRefGoogle Scholar
  16. 16.
    Lin, F.J., Wai, R.J.: Hybrid control using recurrent fuzzy neural networks for linear induction motor servo drive. IEEE Transactions on Fuzzy system 9, 102–115 (2001)CrossRefGoogle Scholar
  17. 17.
    Lai, Y.S., Lin, J.C.: New hybrid fuzzy controller for direct torque control induction motor drives. IEEE Transactions on Power Electronics 18, 1211–1219 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Muammer Gökbulut
    • 1
  • Beşir Dandil
    • 1
  • Cafer Bal
    • 1
  1. 1.Faculty of Technical Edu., Dep. of Electr. and Computer Science ElazigFirat U.Turkey

Personalised recommendations