Skip to main content
SpringerLink
Log in

Novel Load Disturbance Observer-based Global Complementary Sliding Mode Control for a Precision Motion Stage Driven by PMLSM

  • Regular Papers
  • Control Theory and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

In this paper, a novel load disturbance observer-based global complementary sliding mode control (NLDOB-GCSMC) method is proposed to achieve high servo performance of the precision motion stage driven by a permanent magnet linear synchronous motor (PMLSM). The global complementary sliding mode control (GCSMC) method incorporates an approach angle into the saturation function to realize the dynamic change of the boundary layer, which guarantees the asymptotic stability and improves the global robustness of the system. Besides, compared to hybrid CSMC strategies, the GCSMC method has a simpler structure and faster response speed. However, in practical applications, the load disturbance has a significant impact on system performance. Therefore, a novel load disturbance observer (NLDOB) capable of identifying the load variation is proposed. According to the model reference adaptive identification (MRAI) theory, NLDOB can identify the load disturbance on-line and realize compensation to further improve the robustness. The more accurate tracking performance and stronger robustness of the proposed control scheme compared to conventional approaches have been confirmed through comparative experimental studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. M. Rahman, A. Al Mamun, and K. Yao, “Discrete time adaptive controller for suppression of resonance in hard disk drive servo system,” International Journal of Control, Automation and Systems, vol. 13, no. 5, pp. 1161–1172, Dec. 2015.

    Article  Google Scholar 

  2. B. Song, S. Zheng, X. Tang, and W. Qiao, “Fractional order modeling and nonlinear fractional order pi-type control for PMLSM system,” Asian Journal of Control, vol. 1, no. 2, pp. 99–108, Jul 2017.

    MathSciNet  MATH  Google Scholar 

  3. X. Dang, X. Xu, and X. Yu, “Control for PMLSM based on wavelet neural network,” Electric Machines and Control, vol. 17, no. 1, pp. 43–50, Jan 2013.

    Google Scholar 

  4. N. Vu, D. Yu, B. Han, and J. Jung, “T-S fuzzy-model-based sliding-mode control for surface-mounted permanentmagnet synchronous motors considering uncertainties,” IEEE Transactions on Industrial Electronics, vol. 60, no. 10, pp. 4281–4291, Nov 2013.

    Article  Google Scholar 

  5. A. Maouche, M. Mohammed, B. Bensaker, and M. Farza, “High gain adaptive observer design for sensorless state and parameter estimation of induction motors,” International Journal of Control, Automation and Systems, vol. 23, no. 5, pp. 1106–1117, 2015.

    Article  Google Scholar 

  6. C. Fu, L. Zhao, J. Yu, and H. Yu, “Neural network-based command filtered control for induction motors with input saturation,” IET Control Theory & Applications, vol. 11, no. 15, pp. 2636–2642, Jun 2017.

    Article  MathSciNet  Google Scholar 

  7. S. Chen, H. Chiang, T. Liu, and C. Chan, “Precision motion control of permanent magnet linear synchronous motors using adaptive fuzzy fractional-order sliding-mode control,” IEEE/ASME Transactions on Mechatronics, vol. 24, no. 2, pp. 741–452, 2019.

    Article  Google Scholar 

  8. F. El-Sousy and K. Abuhasel, “Intelligent adaptive dynamic surface control system with recurrent wavelet El-man neural networks for DSP-based induction motor servo drives,” IEEE Transactions on Industry Applications, vol. 55, no. 2, pp. 1988–2020, 2019.

    Article  Google Scholar 

  9. Y. Wang, X. Li, and X. Chen, “A robust adaptive neural network control method based on permanent magnetic linear synchronous motor for the reticle stage of lithography,” Transactions of China Electrotechnical Society, vol. 31, no. 6, pp. 38–46, May 2016.

    Google Scholar 

  10. J. Yu, Y. Ma, H. Yu, and C. Lin, “Adaptive fuzzy dynamic surface control for induction motors with iron losses in electric vehicle drive systems via backstepping,” Information Sciences, vol. 376, no. 10, pp. 172–189, Jan 2017.

    Article  Google Scholar 

  11. E. Fayez and K. Abuhasel, “Self-organizing recurrent fuzzy wavelet neural network-based mixed H2/H∞ adaptive tracking control for uncertain two-axis motion control system,” IEEE Transactions on Industry Applications, vol. 52, no. 6, pp. 5139–5155, Nov 2016.

    Article  Google Scholar 

  12. S. Chen and T. Liu, “Intelligent tracking control of a permanent magnet linear synchronous motor using self-evolving probabilistic fuzzy neural network,” IET Electric Power Applications, vol. 11, no. 6, pp. 1043–1054, Feb 2017.

    Article  Google Scholar 

  13. C. Ting, J. Lieu, C. Liu, and R. Hsu, “An adaptive FNN control design of PMLSM in stationary reference frame,” Journal of Control, Automation and Electrical Systems, vol. 27, no. 4, pp. 391–405, Aug 2016.

    Article  Google Scholar 

  14. H. Han, W. Wang, and Z. Bai, “Global robust terminal sliding mode control for PMLSM servo system,” Applied Mechanics & Materials, vol. 273, pp. 280–285, Jan 2013.

    Article  Google Scholar 

  15. D. Ye, H. Zhang, Y. Tian, and Z. Sun, “Fuzzy sliding mode control of nonparallel-ground-track imaging satellite with high precision,” International Journal of Control, Automation and Systems, vol. 18, no. 6, pp. 1617–1628, 2020.

    Article  Google Scholar 

  16. Q. Xu, “Precision motion control of piezoelectric nanopositioning stage with chattering-free adaptive sliding mode control,” IEEE Transactions on Automation Science and Engineering, vol. 14, no. 1, pp. 238–248, Jan 2017.

    Article  Google Scholar 

  17. A. Humaidi and A. Hameed, “PMLSM position control based on continuous projection adaptive sliding mode controller,” Systems Science & Control Engineering, vol. 6, no. 3, pp. 242–252, Nov 2018.

    Article  Google Scholar 

  18. Y. Huang, Z. Zhang, W. Huang, and S. Chen, “DC-Link voltage regulation for wind power system by complementary sliding mode control,” IEEE Access, vol. 7, pp. 22773–22780, 2019.

    Article  Google Scholar 

  19. X. Zhao and H. Jin, “Complementary sliding mode control for permanent magnet linear synchronous motor based on Elman neural network,” Transactions of China Electrotechnical Society, vol. 33, no. 5, pp. 220–226, Mar 2018.

    Google Scholar 

  20. F. Lin, J. Hwang, P. Chou and Y. Hung, “FPGA-based intelligent complementary sliding mode control for PMLSM servo drive system,” IEEE Transactions on Power Electronics, vol. 25, no. 10, pp. 2573–2587, Oct 2010.

    Article  Google Scholar 

  21. L. Wang, Z. Wu, and C. Liu, “Optimal parameter load disturbance compensation method of permanent magnet linear servo system,” Transactions of China Electrotechnical Society, vol. 27, no. 3, pp. 133–138, Mar 2012.

    Google Scholar 

  22. C. Lin, H. Yau, and Y. Tian, “Identification and compensation of nonlinear friction characteristics and precision control for a linear motor stage,” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 4, pp. 1385–1396, May 2012.

    Article  Google Scholar 

  23. K. Cho, J. Kim, and S. Choi, “A high-precision motion control based on a periodic adaptive disturbance observer in a PMLSM,” IEEE/ASME Transactions on Mechatronics, vol. 20, no. 5, pp. 2158–2171, Oct 2015.

    Article  Google Scholar 

  24. C. Chen and W. Lin, “Self-adaptive interval type-2 neural fuzzy network control for PMLSM drives,” Expert Systems with Applications, vol. 39, no. 12, pp. 14679–14689, Nov 2011.

    Article  Google Scholar 

  25. S. Nasar and I. Boldea, “Linear electric motors,” Theory, Design, and Practical Applications, Prentice-Hall, Engle-wood Cliffs, NJ, 1987.

    Google Scholar 

  26. K. Buckholtz, Sliding Mode Control Using a Switching Function Incorporating the State Trajectory Approach Angle, Ph.D. Dissertation, University of Dayton, 2001.

  27. L. Liu, Analysis and Design of Sliding Mode Control Systems, Science Press, Beijing, China, 2017.

    Google Scholar 

  28. J. P. Su and C. C. Wang, “Complementary sliding control of non-linear system,” International Journal of Control, vol. 75, no. 5, pp. 360–368, 2002.

    Article  MathSciNet  Google Scholar 

  29. J. J. E. Slotine, “Sliding controller design for non-linear systems,” International Journal of Control, vol. 40, pp. 421–434, 1984.

    Article  Google Scholar 

  30. Y. Liu, B. Zhou, and S. Fang, “Sliding mode control of PMSM based on a novel disturbance observer,” Proceedings of the CSEE, vol. 30, no. 9, pp. 80–85, Mar 2010.

    Google Scholar 

  31. M. Lin, Y. Li, C. Wu, G. Yuan, and C. Li, “A model reference adaptive system based sliding mode observer for model predictive controlled permanent magnet synchronous motor drive,” Transactions of China Electrotechnical Society, vol. 32, no. 6, pp. 156–163, Mar 2017.

    Google Scholar 

  32. Y. Shen and Y. Liu, “Identification of PMSM based on improved model reference self-adaptive algorithm,” Electric Drive, vol. 39, no. 5, pp. 47–50, 2009.

    Google Scholar 

  33. M. Lin, Y. Li, C. Wu, G. Yuan and C. Li, “A model reference adaptive system based sliding mode observer for model predictive controlled permanent magnet synchronous motor drive,” Transactions of China Electrotechnical Society, vol. 32, no. 6, pp. 156–163, 2017.

    Google Scholar 

  34. L. Qiao and W. Zhang, “Double-loop integral terminal sliding mode tracking control for UUVs with adaptive dynamic compensation of uncertainties and disturbances,” IEEE Journal of Oceanic Engineering, vol. 44, no. 1, pp. 29–53, Jan, 2019.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineering, Shenyang University of Technology, No. 111, Shenliao West Road, Economic & Technological Development Zone, Shenyang, 110870, China

    Hongyan Jin, Ximei Zhao & Tianhe Wang

Authors
  1. Hongyan Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ximei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tianhe Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ximei Zhao.

Additional information

Recommended by Associate Editor Ning Sun under the direction of Editor Yoshito Ohta.

This work was supported by National Natural Science Foundation of China under Grant 51175349 and Key Projects of Liaoning Provincial Natural Science Foundation Plans under Grant 20170540677.

Hongyan Jin was born in Shenyang, Liaoning, China, in 1993. She received her B.S. degree in electrical engineering from Shenyang University of Technology, Shenyang, China, in 2016. She is currently working toward a Ph.D. degree in electrical engineering in Shenyang University of Technology. Her research interests include motor control and intelligent control.

Ximei Zhao was born in Changchun, Jilin, China, in 1979. She received her B.S., M.S. and Ph.D. degrees in electrical engineering from Shenyang University of Technology, Shenyang, China, in 2003, 2006, and 2009, respectively. She is currently a professor and a doctoral supervisor with the School of Electrical Engineering in Shenyang University of Technology. Her research interests are electrical machines, motor drives, motor control, intelligent control and robot control. She has authored or coauthored more than 100 technical papers, 3 textbooks and holds 15 patents in these areas.

Tianhe Wang was born in Shenyang, Liaoning, China, in 1993. He received his B.S. degree in automation from Dalian Jiaotong University, Dalian, China, in 2016. He is currently working toward an M.S. degree in electrical engineering in Shenyang University of Technology. His research interests include motor control and intelligent control.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jin, H., Zhao, X. & Wang, T. Novel Load Disturbance Observer-based Global Complementary Sliding Mode Control for a Precision Motion Stage Driven by PMLSM. Int. J. Control Autom. Syst. 19, 3676–3687 (2021). https://doi.org/10.1007/s12555-020-0360-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-020-0360-6

Keywords

Search

Navigation