Skip to main content
SpringerLink
Log in

High precision tracking control for linear servo system based on intelligent second-order complementary sliding mode

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

To develop a high-performance permanent magnet linear servo system, a novel intelligent second-order complementary sliding mode control (ISOCSMC) method is proposed in this paper. First, the mathematical model of permanent magnet linear synchronous motor (PMLSM) with uncertainties is established. To conquer the uncertainties and reduce chattering, a second-order complementary sliding mode control (SOCSMC) with fast convergence and global robustness is developed. However, because the value of lumped uncertainty is difficult to obtain in advance and cannot be adjusted automatically, an intelligent control system using recurrent Gegenbauer fuzzy neural network (RGFNN) based on improved whale optimization algorithm (IWOA) is proposed to further improve the servo performance. RGFNN acts as an estimator to approximate the lumped uncertainty, and IWOA is used to convergence of the output tracking error and improve the convergence speed of RGFNN. Finally, the experimental results demonstrate the proposed controller exhibits high tracking accuracy and strong robustness against the parameter variations and load disturbances in comparison with the recently reported control strategies.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Song B, Zheng S, Tang X et al (2017) Fractional order modeling and nonlinear fractional order pi-type control for PMLSM system. Asian J Control 1(2):99–108

    MathSciNet  Google Scholar 

  2. Chen SY, Liu TS (2017) Intelligent tracking control of a PMLSM using self-evolving probabilistic fuzzy neural network. IET Electr Power Appl 11(6):1043–1054

    Article  Google Scholar 

  3. El-Sousy F and Abuhasel KA (2018) Intelligent adaptive dynamic surface control system with recurrent wavelet Elman neural networks for DSP-based induction motor servo drives. IEEE Trans Ind Appl. https://doi.org/10.1109/TIA.2018.2876642

  4. Li T, Sun X, Lei G et al (2022) Finite-control-set model predictive control of permanent magnet synchronous motor drive systems-an overview. IEEE/CAA J Autom Sinica (JAS) 9(12):2087–2105

    Article  Google Scholar 

  5. Sun X, Li T, Zhu Z et al (2021) Speed sensorless model predictive current control based on finite position set for PMSHM drives. IEEE Trans Transp Electrific 7(4):2743–2752

    Article  Google Scholar 

  6. Nguyen TH, Nguyen TT, Le KM (2023) An adaptive backstepping sliding-mode control for improving position tracking of a permanent-magnet synchronous motor with a nonlinear disturbance observer. IEEE Access 11:19173–19185

    Article  Google Scholar 

  7. Nguyen TH, Nguyen TT, Nguyen VQ et al (2022) An adaptive sliding-mode controller with a modified reduced-order proportional integral observer for speed regulation of a permanent magnet synchronous motor. IEEE Trans Industr Electron 69(7):7181–7191

    Article  Google Scholar 

  8. Zheng J, Wang H, Man Z et al (2015) Robust motion control of a linear motor positioner using fast nonsingular terminal sliding mode. IEEE/ASME Trans Mechatron 20(4):1743–1752

    Article  Google Scholar 

  9. Shao K, Zheng J, Huang K et al (2020) Finite-time control of a linear motor positioner using adaptive recursive terminal sliding mode. IEEE Trans Industr Electron 67(8):6659–6668

    Article  Google Scholar 

  10. Zhang X, Chen Z, Pan J et al (2006) Fixed boundary layer sliding mode control of permanent magnet linear synchronous motor. Proc CSEE 26(22):115–121

    CAS  Google Scholar 

  11. Song Q, Li Y, Jia C (2018) A novel direct torque control method based on asymmetric boundary layer sliding mode control for PMSM. Energies 11(3):657

    Article  Google Scholar 

  12. Su JP, Wang CC (2002) Complementary sliding control of nonlinear systems. Int J Control 75(5):360–368

    Article  Google Scholar 

  13. Lin FJ, Hung YC, Ruan KC (2014) An intelligent second-order sliding-mode control for an electric power steering system using a wavelet fuzzy neural network. IEEE Trans Fuzzy Syst 22(6):1598–1611

    Article  Google Scholar 

  14. Lin FJ, Chen SG and Hsu CW (2018) Intelligent backstepping control using recurrent feature selection fuzzy neural network for synchronous reluctance motor position servo drive system. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2018.2858749

  15. Lin FJ, Chen SY, Huang MS (2011) Adaptive complementary sliding-mode control for thrust active magnetic bearing system. Control Eng Pract 19:711–722

    Article  Google Scholar 

  16. Lin CH (2017) Nonlinear backstepping control design of LSM drive system using adaptive modified recurrent Laguerre orthogonal polynomial neural network. Int J Control Autom Syst 15(2):905–917

    Article  Google Scholar 

  17. Jin Z, Sun X, Lei G et al (2022) Sliding mode direct torque control of SPMSMs based on a hybrid wolf optimization algorithm. IEEE Trans Industr Electron 69(5):4534–4544

    Article  ADS  Google Scholar 

  18. Miejalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  19. Aljarah I, Faris H, Mirjalili S (2018) Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput 28(1):1–15

    Article  Google Scholar 

  20. Qais MH, Hasanien HM, Alghuwainem S (2020) Whale optimization algorithm-based Sugeno fuzzy logic controller for fault ride-through improvement of grid-connected variable speed wind generators. Eng Appl Artif Intell 87:1–13

    Article  Google Scholar 

  21. Rui Y, Wang M, Li L et al (2020) Robust predictive current control of PMLSM with extended state modeling based Kalman filter: for time-varying disturbance rejection. IEEE Trans Power Electron 35(2):2208–2221

    Article  ADS  Google Scholar 

  22. Xie Y (2018) Second order complementary sliding mode speed control of permanent magnet synchronous motor. Fuzhou University

  23. Lin FJ, Huang MS, Chen SG et al (2019) Adaptive backstepping control for synchronous reluctance motor based on intelligent current angle control. IEEE Trans Power Electron 34(12):7465–7479

    Google Scholar 

  24. Lin CH (2016) Blend modified recurrent Gegenbauer orthogonal polynomial neural network control for six-phase copper rotor induction motor servo-driven continuously variable transmission system using amended artificial bee colony optimization. Trans Inst Meas Control 39(6):1–30

    ADS  Google Scholar 

  25. Nguyen TT, Tran HN, Nguyen TH et al (2023) Recurrent neural network-based robust adaptive model predictive speed control for PMSM with parameter mismatch. IEEE Trans Industr Electron 70(6):6219–6228

    Article  Google Scholar 

  26. Loucif F, Kechida S, Abdennour S (2020) Whale optimizer algorithm to tune PID controller for the trajectory tracking control of robot manipulator. J Braz Soc Mech Sci Eng 42(1):1–11

    Article  Google Scholar 

Download references

Acknowledgements

The author would like to acknowledge the financial supports of the Liaoning Provincial Doctoral Research Start-up Foundation Plan, under Grant 2022-BS-177.

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, Yunwei Gong & Ximei Zhao

Authors
  1. Hongyan Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yunwei Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ximei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

This manuscript was written by Hongyan Jin, who independently completed the research design and data analysis and wrote the article. Yunwei Gong proofread all drafts, while Ximei Zhao provided the first guidance and objectively reviewed the article. All authors have contributed to further revisions of this article. Finally, all authors made contributions to the writing of the manuscript, providing constructive suggestions and improvement suggestions to ensure that the article can accurately express complex research results.

Corresponding author

Correspondence to Hongyan Jin.

Ethics declarations

Conflict of interest

The authors declare that there is no competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jin, H., Gong, Y. & Zhao, X. High precision tracking control for linear servo system based on intelligent second-order complementary sliding mode. Electr Eng 106, 1105–1120 (2024). https://doi.org/10.1007/s00202-023-02038-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00202-023-02038-4

Keywords

Search

Navigation