Skip to main content
SpringerLink
Log in

Observer-based finite-time adaptive neural network control for PMSM with state constraints

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This article investigates the observer-based finite-time adaptive neural network control for the permanent magnet synchronous motor (PMSM) system. The addressed PMSM system includes unknown nonlinear dynamics and constraint immeasurable states. The neural networks are utilized to approximate the unknown nonlinear dynamics and an equivalent control design model is established, by which a neural network state observer is given to estimate the immeasurable states. By constructing barrier Lyapunov functions and under the framework of adaptive backstepping control design technique and finite-time stability theory, a finite-time adaptive neural network control scheme is developed. It is proved that the proposed control scheme ensures the closed-loop system stable and the angular velocity, stator current and other state variables not to exceed their predefined bounds in a finite time. Finally, the computer simulation and a comparison with the existing controller are provided to confirm the effectiveness of the presented controller.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data Availability

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

References

  1. Chaoui H, Khayamy M, Okoye O (2018) Adaptive RBF network based direct voltage control for interior PMSM based vehicles. IEEE Trans Veh Technol 67(7):5740–5749

    Google Scholar 

  2. Dai Y, Ni S, Xu D (2021) Disturbance-observer based prescribed-performance fuzzy sliding mode control for PMSM in electric vehicles. Eng Appl Artif Intell 104:104361

    Article  Google Scholar 

  3. Fung RF, Kung YS, Wu GC (2010) Dynamic analysis and system identification of an LCD glass-handling robot driven by a PMSM. Appl Math Model 34(5):1360–1381

    Article  MathSciNet  MATH  Google Scholar 

  4. Yuan T, Wang D, Wang X (2019) High-precision servo control of industrial robot driven by PMSM-DTC utilizing composite active vectors. IEEE Access 7:7577–7587

    Article  Google Scholar 

  5. Zhou J, Wang Y (2002) Adaptive backstepping speed controller design for a permanent magnet synchronous motor. IEE Pro-Elec Power Appl 149(2):165–172

    Article  Google Scholar 

  6. Morawiec M (2013) The adaptive backstepping control of permanent magnet synchronous motor supplied by current source inverter. IEEE Trans Industr Inf 9(2):1047–1055

    Article  Google Scholar 

  7. Liu J, Li H, Deng Y (2017) Torque ripple minimization of PMSM based on robust ILC via adaptive sliding mode control. IEEE Trans Power Electron 33(4):3655–3671

    Article  Google Scholar 

  8. Chaoui H, Sicard P (2011) Adaptive Lyapunov-based neural network sensorless control of permanent magnet synchronous machines. Neural Comput Appl 20(5):717–727

    Article  Google Scholar 

  9. Deniz E (2017) ANN-based MPPT algorithm for solar PMSM drive system fed by direct-connected PV array. Neural Comput Appl 28(10):3061–3072

    Article  Google Scholar 

  10. Yu J, Ma Y, Chen B (2011) Adaptive fuzzy backstepping position tracking control for a permanent magnet synchronous motor. Int J Innov Comput Inform Control 7(4):1589–1602

    Google Scholar 

  11. Li S, Gu H (2012) Fuzzy adaptive internal model control schemes for PMSM speed-regulation system. IEEE Trans Industr Inf 8(4):767–779

    Article  Google Scholar 

  12. Mao W, Liu G (2019) Development of an adaptive fuzzy sliding mode trajectory control strategy for two-axis PMSM-driven stage application. Int J Fuzzy Syst 21(3):793–808

    Article  MathSciNet  Google Scholar 

  13. Gao J, Shi L, Deng L (2017) Finite-time adaptive chaos control for permanent magnet synchronous motor. J Comput Appl 37(2):597–601

    Google Scholar 

  14. Wang XJ, Wang SP (2016) Adaptive fuzzy robust control of PMSM with smooth inverse based dead-zone compensation. Int J Control Autom Syst 14(2):378–388

    Article  Google Scholar 

  15. Chang W, Tong S (2017) Adaptive fuzzy tracking control design for permanent magnet synchronous motors with output constraint. Nonlinear Dyn 87(1):291–302

    Article  MATH  Google Scholar 

  16. Liu Y, Yu J, Yu H, Lin C, Zhao L (2017) Barrier Lyapunov functions based adaptive neural control for permanent magnet synchronous motors with full state constraints. IEEE Access 5:10382–10389

    Article  Google Scholar 

  17. Sakthivel R, Santra S, Kaviarasan B (2016) Finite-time sampled-data control of permanent magnet synchronous motor systems. Nonlinear Dyn 86(3):2081–2092

    Article  MATH  Google Scholar 

  18. Yu J, Shi P, Dong W, Chen B, Lin C (2014) Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors. IEEE Trans Neural Networks Learn Syst 26(3):640–645

    Article  MathSciNet  Google Scholar 

  19. Yang X, Yu J, Wang Q, Zhao L, Yu H, Lin C (2019) Adaptive fuzzy finite-time command filtered tracking control for permanent magnet synchronous motors. Neurocomputing 337(14):110–119

    Article  Google Scholar 

  20. Lu S, Wang X, Wang L (2020) Finite-time adaptive neural network control for fractional-order chaotic PMSM via command filtered backstepping. Adv Difference Equ 1:1–21

    MathSciNet  MATH  Google Scholar 

  21. Sun Y, Wu X, Bai L, Wei Z, Sun G (2016) Finite-time synchronization control and parameter identification of uncertain permanent magnet synchronous motor. Neurocomputing 207:511–518

    Article  Google Scholar 

  22. Chen Q, Ren X, Na J (2017) Adaptive robust finite-time neural control of uncertain PMSM servo system with nonlinear dead zone. Neural Comput Appl 28(12):3725–3736

    Article  Google Scholar 

  23. Lee H, Lee J (2012) Design of iterative sliding mode observer for sensorless PMSM control. IEEE Trans Control Syst Technol 21(4):1394–1399

    Article  Google Scholar 

  24. Wang H, Li S, Lan Q (2017) Continuous terminal sliding mode control with extended state observer for PMSM speed regulation system. Trans Inst Meas Control 39(8):1195–1204

    Article  Google Scholar 

  25. Zhao Y, Liu X, Zhang Q (2019) Predictive speed-control algorithm based on a novel extended-state observer for PMSM drives. Appl Sci 9(12):2575

    Article  Google Scholar 

  26. Li S, Liu H, Ding S (2010) A speed control for a PMSM using finite-time feedback control and disturbance compensation. Trans Inst Meas Control 32(2):170–187

    Article  Google Scholar 

  27. Wang T, Tong S, Li Y (2013) Adaptive neural network output feedback control of stochastic nonlinear systems with dynamical uncertainties. Neural Comput Appl 23(5):1481–1494

    Article  Google Scholar 

  28. Tang L, Liu Y, Tong S (2014) Adaptive neural control using reinforcement learning for a class of robot manipulator. Neural Comput Appl 25(1):135–141

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (under Grant Nos. 62173172 and U22A2043) and in part by the Key Laboratory of Intelligent Manufacturing Technology (Shantou University), Ministry of Education (under grant No. 202109244).

Author information

Authors and Affiliations

  1. College of Science, Liaoning University of Technology, Shiying, Jinzhou, 121001, Liaoning, China

    Sihui Zhou, Shuai Sui, Yongming Li & Shaocheng Tong

Authors
  1. Sihui Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuai Sui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yongming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaocheng Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaocheng Tong.

Ethics declarations

Conflict of interest

The authors declared that they have no conflicts of interest to this work.

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

Zhou, S., Sui, S., Li, Y. et al. Observer-based finite-time adaptive neural network control for PMSM with state constraints. Neural Comput & Applic 35, 6635–6645 (2023). https://doi.org/10.1007/s00521-022-08050-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-08050-2

Keywords

Search

Navigation