DOI QR코드

DOI QR Code

Torque Ripple Minimization of PMSM Using Parameter Optimization Based Iterative Learning Control

  • Xia, Changliang (School of Electrical Engineering and Automation, Tianjin University) ;
  • Deng, Weitao (School of Electrical Engineering and Automation, Tianjin University) ;
  • Shi, Tingna (School of Electrical Engineering and Automation, Tianjin University) ;
  • Yan, Yan (School of Electrical Engineering and Automation, Tianjin University)
  • Received : 2015.04.04
  • Accepted : 2015.11.19
  • Published : 2016.03.01

Abstract

In this paper, a parameter optimization based iterative learning control strategy is presented for permanent magnet synchronous motor control. This paper analyzes the mechanism of iterative learning control suppressing PMSM torque ripple and discusses the impact of controller parameters on steady-state and dynamic performance of the system. Based on the analysis, an optimization problem is constructed, and the expression of the optimal controller parameter is obtained to adjust the controller parameter online. Experimental research is carried out on a 5.2kW PMSM. The results show that the parameter optimization based iterative learning control proposed in this paper achieves lower torque ripple during steady-state operation and short regulating time of dynamic response, thus satisfying the demands for both steady state and dynamic performance of the speed regulating system.

Keywords

References

  1. Z. Q. Zhu, S. Ruangsinchaiwanich, and D. Howe, “Synthesis of cogging torque waveform from analysis of a single stator slot,” IEEE Trans. Ind. Appl., vol. 42, no. 3, pp. 650-657, May-June 2006. https://doi.org/10.1109/TIA.2006.872930
  2. C. L. Xia, J. X. Zhao, Y. Yan, and T. N. Shi, “A novel direct torque control of matrix converter-fed PMSM drives using duty cycle control for torque ripple reduction,” IEEE Trans. Ind. Electron., vol. 61, no. 6, pp. 2700-2713, June 2014. https://doi.org/10.1109/TIE.2013.2276039
  3. S. T. Chen, C. Namuduri, and S. Mir, “Controller-induced parasitic torque ripples in a PM synchronous motor,” IEEE Trans. Ind. Appl., vol. 38, no. 5, pp. 1273-1281, October 2002. https://doi.org/10.1109/TIA.2002.803000
  4. D. Zarko, D. Ban, and T. A. Lipo, “Analytical solution for cogging torque in surface permanent magnet motors using conformal mapping,” IEEE Trans. Magn., vol. 44, no. 1, pp. 52-65, January 2008. https://doi.org/10.1109/TMAG.2007.908652
  5. P. S. Shin, H. Y. Kim and Y. B. Kim, “Minimization of Torque Ripple for an IPMSM with a Notched Rotor Using the Particle Swarm Optimization Method,” JEET, vol. 9, no. 5, pp. 1577-1581, September 2014.
  6. T. Jahns, and W. Soong, “Pulsating torque minimization techniques for permanent magnet AC motor drives – a review,” IEEE Trans. Ind. Electron., vol. 43, no. 2, pp. 321-330, April 1996. https://doi.org/10.1109/41.491356
  7. G. Ferretti, G. Magnani, and P. Rocco, “Modeling, identification, and compensation of pulsating torque in permanent magnet ac motors,” IEEE Trans. Ind. Electron., vol. 45, no. 6, pp. 912-920, Dec. 1998.
  8. N. Vaks, D. Horvath, “Feedback-based mitigation of torque harmonics in interior permanent magnet synchronous machines,” in IEMDC., pp. 564-570, May 2013.
  9. D. Flieller, N. K. Nguyen,etc., “A self-learning solution for torque ripple reduction for non-sinusoidal permanent magnet motor drives based on artificial neural networks,” IEEE Trans. Ind. Electron., vol. 61, no. 61, pp. 655-666, Feb. 2014. https://doi.org/10.1109/TIE.2013.2257136
  10. M. Ruderman, A. Ruderman, and T. Bertram, “Observer-based compensation of additive periodic torque disturbances in permanent magnet motors,” IEEE Trans. Ind. Informat., vol. 9, no. 2, pp. 1130-1138, May 2013. https://doi.org/10.1109/TII.2012.2222040
  11. N. Matsui, T. Makino, and H. Satoh, “Autocompensation of torqueripple of direct drive motor by torque observer,” IEEE Trans. Ind. Appl., vol. 29, no. 1, pp. 187-194, Jan/Feb 1993. https://doi.org/10.1109/28.195906
  12. Y. Abdel-Rady I. Mohamed, and E. F EI-Saadany, “A current control scheme with an adaptive internal model for torque ripple minimization and robust current regulation in PMSM drive systems,” IEEE Trans. energy convers, vol. 23, no. 1, pp. 92-100, March 2008. https://doi.org/10.1109/TEC.2007.914352
  13. Y.A.I. Mohamed, “A newly designed instantaneous-torque control of direct-drive PMSM servo actuator with improved torque estimation and control characteristics,” IEEE Trans. Ind. Electron., vol. 54, no. 5, pp. 2864-2873, Oct. 2007. https://doi.org/10.1109/TIE.2007.901356
  14. G. Jayabaskaran, B. Adhavan, and V. Jagannathan, “Torque ripple reduction in permanent magnet synchronous motor driven by field oriented control using iterative learning control with space vector pulse width modulation,” in ICCCI., Coimbatore, 2013.
  15. J. X. Xu, P. S. K, Y. J. Pan. T. Heng, and Lee Lam B. H, “A modular control scheme for PMSM speed control with pulsating torque minimization,” IEEE Trans. Ind. Electron., vol. 51, no. 3, pp. 526-536, June 2004. https://doi.org/10.1109/TIE.2004.825365
  16. B. H. Lam, U. Nat, S. K. Panda, and J. X. Xu, “Reduction of periodic speed ripples in PM synchronous motors using iterative learning control,” in IECON, vol. 2, pp. 1406-1411, 2000.
  17. K. C. Yeo, G. Heins and F. D. Boer, “Position Based Iterative Learning Control to minimise torque ripple for PMSMs,” in IECON 2001, Melbourne, Australia, Nov. 2001.
  18. Y. Yan, W. Li, W. Deng and G. Zhang, “Torque Ripple Minimization of PMSM Using PI Type Iterative Learning Control,” in IECON 2014, Dallas, USA, Nov. 2014.
  19. K. Abidi and J. Xu, “Iterative Learning Control for Sampled-Data Systems: From Theory to Practice,” IEEE Trans. Ind. Electron., vol. 58, no. 7, pp. 3002-3015, July 2011. https://doi.org/10.1109/TIE.2010.2070774
  20. W. Qian, S. K. Panda and J. Xu, “Torque Ripple Minimization in PM synchronous motors using iterative learning control,” IEEE Trans. Power. Electron., vol. 19, no. 2, pp. 272-279, March. 2004.
  21. W. Z. Qian, S. K. Panda, and J. X. Xu, “Speed Ripple Minimization in PM synchronous motor using iterative learning control,” IEEE Trans. energy convers, vol. 20, no. 1, pp. 53-61, March 2005. https://doi.org/10.1109/TEC.2004.841513
  22. N. Amann, D. H. Owens, and E. rogers, “Iterative learning control using optimal feedback and feedforward actions,” Internat. J. Control, vol. 65, pp. 277-293, July 1995.

Cited by

  1. Active torque ripple reduction based on an analytical model of torque vol.11, pp.3, 2017, https://doi.org/10.1049/iet-epa.2016.0475
  2. A Parameter Identification Method Based on Forgetting Factor Dynamic Adjustment for PMSM Applied to the Rapid Control of Satellite Attitude vol.14, pp.1, 2019, https://doi.org/10.1007/s42835-018-00049-x
  3. A New Surrogate-assisted Robust Multi-objective Optimization Algorithm for an Electrical Machine Design pp.2093-7423, 2019, https://doi.org/10.1007/s42835-019-00120-1