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Cluster Computing

, Volume 22, Supplement 2, pp 4775–4784 | Cite as

Parameter estimation for BLDCM in rescue hoist drive and reduction of torque ripple and winding loss using conditional extremum current model

  • Chun FangEmail author
  • Manfeng Dou
  • Bo Tan
  • Jianwei Yang
Article
  • 51 Downloads

Abstract

Brushless dc motor (BLDCM) presents excellent performance in rescue hoist drive because of its high power&torque density. The development of control strategies greatly improve torque ripple and winding loss of BLDCM respectively. However, it is still a challenge to promote comprehensive performance of hoist system such as loss and vibration etc. in the unified control framework. To solve this problem, a comprehensive control strategy of phase current optimization to reduce winding loss and torque ripple simultaneously for BLDCM that utilizing phase current continuous conduction and estimated back-electromotive force (EMF) is proposed. Firstly, with the electromagnetic torque as the constraint, a conditional extremum model of phase current is built to obtain the minimal solution of three-phase reference current. Secondly, considering non-ideal characteristics of the actual back-EMF wave, a sliding-mode observer is designed to estimate the wave online. Experiment results show that under the same load condition, the torque ripple ratio, winding loss and the current harmonics are all lower in proposed strategy than those inconventional two-phase conduction method. Good performance can be achieved by employing the proposed method in non flux-weakening application like the hoisting.

Keywords

BLDCM Current continuous Conditional extremum model Sliding-mode observer Sigmoid Winding loss Current harmonics Commutation torque ripple Non-ideal trapezoidal back-EMF 

References

  1. 1.
    Liu, Y., Zhu, Z.Q., Howe, D.: Instantaneous torque estimation in sensorless direct-torque-controlled brushless DC MOTORS. IEEE Trans. Ind. Appl. 42(5), 1275–1283 (2006)Google Scholar
  2. 2.
    Lu, H., Zhang, L., Qu, W.: A new torque control method for torque ripple minimization of BLDC motors with un-ideal back EMF. IEEE Trans. Power Electron. 23(2), 950–958 (2008)Google Scholar
  3. 3.
    Fang, J., Zhou, X., Liu, G.: Instantaneous torque control of small inductance brushless DC motor. IEEE Trans. Power Electron. 27(12), 4952–4964 (2012)Google Scholar
  4. 4.
    Shakouhi, S.M., Mohamadian, M., Afjei, E.: Torque ripple minimisation control method for a four-phase brushless DC motor with non-ideal back-electromotive force. IET Electr. Power Appl. 7(5), 360–368 (2013)Google Scholar
  5. 5.
    Shi, T., et al.: A torque control strategy for torque ripple reduction of brushless DC motor with nonideal back electromotive force. IEEE Trans. Ind. Electron. 64(6), 4423–4433 (2017)Google Scholar
  6. 6.
    Liu, Y., Zhu, Z.Q., Howe, D.: Direct torque control of brushless DC drives with reduced torque ripple. IEEE Trans. Ind. Appl. 41(2), 599–608 (2005)Google Scholar
  7. 7.
    Fakham, H., Djemai, M., Busawon, K.: Design and practical implementation of a back-emf sliding-mode observer for a brushless dc motor. IET Electr. Power Appl. 2(6), 353–361 (2008)Google Scholar
  8. 8.
    Carlson, R., Lajoie-Mazenc, M., Fagundes, J.C.D.S.: Analysis of torque ripple due to phase commutation in brushless DC machines. IEEE Trans. Ind. Appl. 28(3), 632–638 (1992)Google Scholar
  9. 9.
    Sheng, T., Wang, X., Zhang, J., Deng, Z.: Torque-ripple mitigation for brushless DC machine drive system using one-cycle average torque control. IEEE Trans. Ind. Electron. 62(4), 2114–2122 (2015)Google Scholar
  10. 10.
    Xia, C., Wang, Y., Shi, T.: Implementation of finite-state model predictive control for commutation torque ripple minimization of permanent-magnet brushless DC motor. IEEE Trans. Ind. Electron. 60(3), 896–905 (2013)Google Scholar
  11. 11.
    Bharatkar, S.S., Yanamshetti, R., Chatterjee, D., Ganguli, A.K.: Dual-mode switching technique for reduction of commutation torque ripple of brushless dc motor. IET Electr. Power Appl. 5(1), 193–202 (2011)Google Scholar
  12. 12.
    Boyang, Hu, Sathiakumar, Swamidoss: A novel 180-degree sensorless system of permanent magnet brushless DC motor. J. Circ. Syst. Comput. 21(7), 1634–1643 (2012)Google Scholar
  13. 13.
    Fang, J., Zhou, X., Liu, G.: Precise accelerated torque control for smallinductance brushless DC motor. IEEE Trans. Power Electron. 28(3), 1400–1412 (2013)Google Scholar
  14. 14.
    Jeong, Y.S., et al.: Online minimum-copper-loss control of an interior permanent-magnet synchronous machine for automotive applications. IEEE Trans. Ind. Appl. 42(5), 1222–1229 (2006)Google Scholar
  15. 15.
    Mbayed, R., et al.: Hybrid excitation synchronous motor control in electric vehicle with copper and iron losses minimization. In: IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society. IEEE (2012)Google Scholar
  16. 16.
    Hamza, R., Muhammad, K., Arunkumar, N., González, G.R.: Hash based encryption for keyframes of diagnostic hysteroscopy. IEEE Access (2017).  https://doi.org/10.1109/ACCESS.2017.2762405 Google Scholar
  17. 17.
    Fernandes, S.L., Gurupur, V.P., Sunder, N.R., Arunkumar, N., Kadry, S.: A novel nonintrusive decision support approach for heart rate measurement. Pattern Recognit. Lett. (2017).  https://doi.org/10.1016/j.patrec.2017.07.002 Google Scholar
  18. 18.
    Arunkumar, N., Ramkumar, K., Venkatraman, V., Abdulhay, E., Fernandes, S.L., Kadry, S., Segal, S.: Classification of focal and non focal EEG using entropies. Pattern Recognit. Lett. 94, 112–117 (2017)Google Scholar
  19. 19.
    Arunkumar, N., Ramkumar, K., Venkataraman, V.: A moving window approximate entropy in wavelet framework for automatic detection of the onset of epileptic seizures. Biomed. Res. (2017); Special Issue: ISSN 0970-938XGoogle Scholar
  20. 20.
    Arunkumar, N., Kumar, K.R., Venkataraman, V.: Automatic detection of epileptic seizures using new entropy measures. J. Med. Imaging Health Inform. 6(3), 724–730 (2016)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of AutomationNorthwestern Polytechnical UniversityXi’anChina

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