Articles

Adaptive Backstepping Controller of PMLSM

Published:
December 23, 2019
Authors
View
Keywords
License

Copyright (c) 2019 by the authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How To Cite
Selected Style: APA
Yahiaoui, M., Bousserhane, I. K., Saidi, Y., & Serraoui, M. (2019). Adaptive Backstepping Controller of PMLSM. Recent Innovations in Mechatronics, 6(1), 1-6. https://doi.org/10.17667/riim.2019.1/1.
Abstract

In this paper, a nonlinear adaptive speed controller for permanent magnet linear synchronous motors based on a newly developed adaptive recursive Backstepping control approach for a permanent magnet synchronous motor drive is discussed and analyzed. The Backstepping technique provides a systematic method to address this type of problem. It combines the notion of Lyapunov function and a controller procedure recursively.  The adaptive Backstepping control approach is utilized to obtain the robustness for mismatched parameter uncertainties. The overall stability of the system is shown using Lyapunov stability theorem. The simulation results clearly show that the proposed scheme can track the speed reference.

References
  1. Z. Yun-fei, S. Bao, and C. Xue-dong, “Position/force control with a lead compensator for PMLSM drive system,” The International Journal of Advanced Manufacturing Technology, vol. 30, pp. 1084-1092, 2006.
  2. Y. Zhang, Y.-p. Chen, and W. Ai, “Design strategy for detent force reduction of permanent magnet linear synchronous motor,” Journal of Shanghai University (English Edition), vol. 12, pp. 548-553, 2008.
  3. F.-J. Lin, P.-H. Shen, S.-L. Yang, and P.-H. Chou, “Recurrent radial basis function network-based fuzzy neural network control for permanent-magnet linear synchronous motor servo drive,” IEEE Transactions on magnetics, vol. 42, pp. 3694-3705, 2006.
  4. J. Zhou and Y. Wang, “Adaptive backstepping speed controller design for a permanent magnet synchronous motor,” IEE Proceedings-Electric Power Applications, vol. 149, pp. 165-172, 2002.
  5. Rini George and Arun. S. Mathew, “Speed Control of PMSM using Backstepping Method,” International Journal of Engineering Research & Technology (IJERT), vol. 4 pp. 2365-2371 ,2015.
  6. C. K. Lin, T. H. Liu, and L.C. Fu, “Adaptive backstepping PI slidingmode control for interior permanent magnet synchronous motor drive systems,” in American Control Conference (ACC), 2011, pp. 4075- 4080.
  7. T. H. Liu, Y. C. Lee, and Y. H. Crang, “Adaptive controller design for a linear motor control system,” IEEE transactions on Aerospace and Electronic Systems, vol. 40, pp. 601-616, 2004.
  8. A. R. Maleknia, K. Rahimi, H. A. Zarchi, and J. Soltani, “Robust backstepping control of permanent magnet linear synchronous motor in extended region using artificial neural network,” IEEE International Conference in Industrial Technology (ICIT), 2008, pp. 1-5.
  9. L. Li, J. Hong, H. Wu, Z. Zhao, and X. Li, “Adaptive back-stepping control for the sectioned permanent magnetic linear synchronous motor in vehicle transportation system,” in Vehicle Power and Propulsion Conference, VPPC'08. IEEE, 2008, pp. 1-5.
  10. W. Limei, Z. Xin, and L. Junjie, “Robust controller design for permanent magnet linear synchronous motor drive system based on L2
  11. gain,” IEEE International Conference in Electrical Machines and Systems (ICEMS), 2007, pp. 645-649.
  12. Y.-S. Huang and C.-C. Sung, “Implementation of sliding mode controller for linear synchronous motors based on direct thrust control theory,” IET control theory & applications, vol. 4, pp. 326-338, 2010.
  13. X. Zhang and J. Pan, “Nonlinear robust sliding mode control for PM linear synchronous motors,” in Power Electronics and Motion Control Conference (IPEMC), 2006, pp. 1-5.
  14. F. J. Lin, P.-H. Shen, and Y.-S. Kung, “Adaptive wavelet neural network control for linear synchronous motor servo drive,” IEEE Transactions on magnetics, vol. 41, pp. 4401-4412, 2005.
  15. F. J. Lin, P.-H. Shen, S.-L. Yang, and P.-H. Chou, “Recurrent radial basis function network-based fuzzy neural network control for
  16. permanent-magnet linear synchronous motor servo drive,” IEEE Transactions on magnetics, vol. 42, pp. 3694-3705, 2006.
  17. J. Yang, N. Fa, R. Chen, “H∞Robust Controller Based on Local Feedback Recurrent Neural Network for Permanent Magnet Linear Synchronous Motor,” international Power Electronics and Motion Control Conference, IPEMC 2006.
  18. Y. Yun, “PMSM stabilizer design based on backstepping,” IEEE International Conference in Power Energy and Control (ICPEC), 2013,
  19. pp. 437-442.
  20. Y. S. Kung, C.-C. Huang, and M.-H. Tsai, “FPGA realization of an adaptive fuzzy controller for PMLSM drive,” IEEE Trans. Industrial
  21. Electronics, vol. 56, pp. 2923-2932, 2009.
  22. Y. S. Kung and N. K. Quang, “FPGA-based neural fuzzy controller design for PMLSM drive,” IEEE International Conference in Power Electronics and Drive Systems (PEDS), 2009, pp. 222-227.
  23. M. Yahiaoui, A. Kechich, and I. K. Bouserhane, “Adaptive NonlinearControl of PMLSM,” Electrotehnica, Electronica, Automatica, vol. 65, 2017.
  24. M. Krstic, I. Kanellakopoulos, P.V. Kokotovic,Nonlinear and Adaptive Control Design (Wiley–Interscience, New York, 1995)
  25. H.J. Sussmann, P.V. Kokotovic, The peaking phenomenon and the global stabilization of nonlinear systems. IEEE Transactions on
  26. Automatic Control36, 424–440 (1991)
Database Logos