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ANN-Based Controllers for Improved Performance of BLDC Motor Drives

  • R. ShanmugasundaramEmail author
  • C. Ganesh
  • A. Singaravelan
Conference paper
  • 11 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 665)

Abstract

This paper discusses the development and performance analysis of ANN-based reference model controller and ANN-based self-tuned PID controller for BLDC motor drives. As the BLDC motor drives are nonlinear due to its parameter and load variations, there is a need to develop ANN-based controllers to overcome the problems arising due to nonlinearity in BLDC motor drives. In this paper, ANN-based self-tuned PID controller is developed for speed control of BLDC motor drives and its performance is compared with the standard ANN-based reference model-controlled BLDC motor drives. The unique feature of ANN-based self-tuned PID controller is that it can dynamically change the PID controller gains to provide optimum performance under changing dynamics of BLDC motor drive. Experimental results show that ANN-based self-tuned PID-controlled BLDC motor drives can effectively deal with speed tracking, load variations, and parameter variations.

Keywords

ANN Reference model Self-tuned PID controller BLDC motor Parameter variations 

Notes

Acknowledgements

The authors thank the management and principal of Sri Ramakrishna Engineering College, Coimbatore, for providing support and facilities to carry out this work.

References

  1. 1.
    Shanmugasundram, R., Zakariah, K.M., Yadaiah, N.: Implementation and performance analysis of digital controllers for brushless dc motor drives. IEEE/ASME Trans. Mechatron. 19(1), 213–224 (2014)Google Scholar
  2. 2.
    Krishnan, R.: Permanent Magnet Synchronous and Brushless DC Motor Drives: Theory, Operation, Performance, Modeling, Simulation, Analysis, and Design-Part 3. Permanent Magnet Brushless DC Machines and their Control, pp. 451–563. CRC Press, Boca Raton, FL (2009)Google Scholar
  3. 3.
    Pillay, P., Krishnan, R.: Modeling, simulation, and analysis of permanent-magnet motor drives, part ii: the brushless dc motor drive. IEEE Trans. Ind. Appl. 25(2), 274–279 (1989)Google Scholar
  4. 4.
    Shanmugasundram, R., Zakariah, K.M., Yadaiah, N.: Low-cost high performance brushless dc motor drive for speed control applications. In: Proceedings of IEEE International Conference on Advanced Recent Technology Communication Computer, Kottayam, Kerala, India, pp. 456–460 (2009)Google Scholar
  5. 5.
    Shanmugasundram, R., Zakariah, K.M., Yadaiah, N.: Digital implementation of fuzzy logic controller for wide range speed control of brushless dc motor. In: Proceedings of IEEE International Conference Vehicle Electronics Safety, Pune, India, pp. 119–124 (2009)Google Scholar
  6. 6.
    Wu, H.-x., Cheng, S.-k., and Cui, S.-m.: A controller of brushless DC motor for electric vehicle. IEEE Trans. Magn. 41(1), 509–513 (2005)Google Scholar
  7. 7.
    Shanmugasundram, R., Zakariah, K.M., Yadaiah, N.: Modelling, simulation and analysis of controllers for brushless direct current motor drives. J. Vib. Control 19(8), 1250–1264 (2012)Google Scholar
  8. 8.
    Grabner, H., Amrhein, W., Silber, S., Gruber, W.: Nonlinear feedback control of a bearingless brushless dc motor. IEEE/ASME Trans. Mechatron. 15(1), 40–47 (2010)Google Scholar
  9. 9.
    Oztk, S.B., Toliyat, H.A.: Direct torque and indirect flux control of brushless dc motor. IEEE/ASME Trans. Mechatron. 16(2), 351–360 (2011)Google Scholar
  10. 10.
    Precup, R.-E., Preitl, S., Rudas, I.J., Tomescu, M.L.: Design and experiments for a class of fuzzy controlled servo systems. IEEE/ASME Trans. Mechatron. 13(1), 22–35 (2008)Google Scholar
  11. 11.
    Tu, Y.-W., Ho, M.-T.: Robust second-order controller synthesis for model matching of interval plants and its application to servo motor control. IEEE Trans. Control Syst. Technol. 20(2), 530–537 (2012)Google Scholar
  12. 12.
    Hadef, M., Bourouina, A., Mekideche, M.R.: Parameter identification of a dc motor via moments method. Int. J. Electr. Electron. Eng. 7(2), 159–163 (2008)Google Scholar
  13. 13.
    Rodriguez, F., Emadi, A.: A novel digital control technique for brushless DC motor drives. IEEE Trans. Ind. Electron. 54(5), 2365–2373 (2007)Google Scholar
  14. 14.
    Wallace, A.K., Spee, R.: The effects of motor parameters on the performance of brushless dc drives. IEEE Trans. Power Electron. 5(1), 2–8 (1990)Google Scholar
  15. 15.
    Varatharaju, V.M., Mathur, B.L., Udhyakumar, K.: Speed control of PMBLDC motor using MATLAB/Simulink and effects of load and inertia changes. In: Proceedings of 2nd International Conference on Mechanical and Electrical Technology, Singapore, pp. 543–548 (2010)Google Scholar
  16. 16.
    Rahman, M.A., Hoque, M.A.: On-line self-tuning Ann-based speed control of a PM DC motor. IEEE/ASME Trans. Mechatron. 2(3), 169–177 (1997)Google Scholar
  17. 17.
    Shanmugasundram, R., Zakariah, K.M., Yadaiah, N.: Effect of parameter variations on the performance of direct current (dc) servomotor drives. J. Vib. Control 19(10), 1575–1586 (2012)Google Scholar
  18. 18.
    Ganesh, C., Patnaik, S.K.: Artificial neural network based proportional plus integral plus derivative controller for a brushless dc position control system. J. Vib. Control 18(14), 2164–2175 (2012)Google Scholar
  19. 19.
    Rubaai, A., Ricketts, D., Kankam, M.D.: Development and implementation of an adaptive fuzzy-neural-network controller for brushless drives. IEEE Trans. Ind. Appl. 38(2), 441–447 (2002)Google Scholar
  20. 20.
    Weerasooriya, S., El-Sharkavi, M.A.: Identification and control of dc motor using back-propagation neural networks. IEEE Trans. Energy Convers. 6(4), 663–669 (1991)Google Scholar
  21. 21.
    Melkote, H., Khorrami, F.: Nonlinear adaptive control of direct drive brushless dc motors and applications to robotic manipulators. IEEE/ASME Trans. Mechatron. 4(1), 71–81 (1999)Google Scholar
  22. 22.
    Weerasooriya, S., EI-Sharkawi, M.A.: Adaptive tracking control high performance dc drives. IEEE Trans. Energy Convers. 4(3), 502–508 (1989)Google Scholar
  23. 23.
    Lai, Y.-S., Shyu, F.-S., Chang, Y.-H.: Novel loss reduction pulse width modulation technique for brushless dc motor drives fed by MOSFET inverter. IEEE Trans. Power Electron. 19(6), 1646–1652 (2004)Google Scholar
  24. 24.
    Sathyan, A., Milivojevic, N., Lee, Y.-J., Krishnamurthy, M., Emadi, A.: An FPGA-based novel digital PWM control scheme for BLDC motor drives. IEEE Trans. Ind. Electron. 56(8), 3040–3049 (2009)Google Scholar
  25. 25.
    Murphree, J., Brzezinski, B., Parker, J.: Using a fixed-point digital signal processor as a PID controller. In: Proceedings of American Society for Engineering Education Annual Conference Exposition, Montreal, QC, Canada, pp. 1–8 (2002)Google Scholar
  26. 26.
    Horvat, R., Jezernik, K., Curkovic, M.: An event-driven approach to the current control of a BLDC motor using FPGA. IEEE Trans. Ind. Electron. 61(7), 3719–3726 (2014)Google Scholar
  27. 27.
    Kim, J.-W., Kim, S.W.: Design of incremental fuzzy PI controllers for a gas-turbine plant. IEEE/ASME Trans. Mechatron. 8(3), 410–414 (2003)Google Scholar
  28. 28.
    Nigam, V., Hussain, S., Agarwal, S.N.: A hybrid fuzzy sliding mode controller for a BLDC motor drive. In: 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, Delhi, India, pp. 1–4 (2016)Google Scholar
  29. 29.
    Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Netw. 1(1), 4–27 (1990)Google Scholar
  30. 30.
    El-Sharkawi, M.A., El-Samahy, A.A., El-Sayed, M.L.: High performance drive of dc brushless motors using neural network. IEEE Trans. Energy Convers 9(2), 317–322 (1994)Google Scholar
  31. 31.
    Tipsuwanporn, V., Piyarat, W., Tarasantisuk, C.: Identification and control of brushless dc motors using on-line trained artificial neural networks. In: IEEE International Conference on Power Conversion, Osaka, pp. 1290–1294 (2002)Google Scholar
  32. 32.
    Vinodhini, R., Ganesh, C., Patnaik, S.K.: Genetic Algorithm optimized on-line Neuro-tuned robust position control of BLDC motor. In: IEEE Students’ Conference on Electrical, Electronics and Computer Science, Bhopal, India, pp. 1–4 (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • R. Shanmugasundaram
    • 1
    Email author
  • C. Ganesh
    • 2
  • A. Singaravelan
    • 2
  1. 1.Sri Ramakrishna Engineering CollegeCoimbatoreIndia
  2. 2.New Horizon College of EngineeringBengaluruIndia

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