DOI QR코드

DOI QR Code

Backstepping control of permanent magnet synchronous motors based on load adaptive fuzzy parameter online tuning

  • Yufeng Zhang (Xi'an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security) ;
  • Qi Yan (Xi'an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security) ;
  • Chongchong Ai (Xi'an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security) ;
  • Yuecheng Wang (Xi'an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security) ;
  • Panpan Han (Xi'an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security) ;
  • Qixun Zhou (Xi'an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security) ;
  • Guanghui Du (Xi'an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security)
  • 투고 : 2023.06.27
  • 심사 : 2024.02.28
  • 발행 : 2024.07.20

초록

As a typical nonlinear control method, backstepping control can decouple the mathematical model of permanent magnet synchronous motors. In addition, permanent magnet synchronous motor control systems based on the backstepping method can enhance the control performance of the control system to a certain extent. Furthermore, the design step is easy and simple to implement in engineering practice. However, the long-term wear and tear, aging, high temperature caused by changes in the basic parameters of motor and external load sudden changes as well as other factors will bring interference to the control system, leading to reduced-control accuracy and control performance degradation. To solve this problem, this paper suggests a control strategy combining backstepping control and fuzzy control based on backstepping control. It sets the load adaptive law and utilizes fuzzy control to make online real-time adjustments to the control parameters in the backstepping control. This is done to improve the immunity of interference and stability of the control system in response to the changes in the parameters of the body of the motor and sudden changes of the load. The effectiveness and feasibility of this system is verified by MATLAB simulation and experimental results, which provides a feasible solution for permanent magnet synchronous motor immunity and high-precision control occasions.

키워드

과제정보

This work was supported in part by the National Natural Science Foundation of China under Grant 52177056. This work was supported by Key Research and Development Program of Shaanxi Province, No. 2023-YBGY-368. This work was supported by Xi'an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security, Xi'an 710054, China.

참고문헌

  1. Bouzidi, I., Masmoudi, A., Bianchi, N.: Electromagnetic/thermal design procedure of an aerospace electric propeller. IEEE Trans. Ind. Appl. 51(6), 4364-4371 (2015) https://doi.org/10.1109/TIA.2015.2442524
  2. Chen, Y., Liu, B.: Design and analysis of a five-phase fault-tolerant permanent magnet synchronous motor for aerospace starter-generator system. IEEE Access 7, 135040-135049 (2019) https://doi.org/10.1109/ACCESS.2019.2941447
  3. Rongyun, Z., Changfu, G., Peicheng, S., et al.: Research on chaos control of permanent magnet synchronous motor based on the synthetical sliding mode control of inverse system decoupling. J. Vib. Control 27(9-10), 1009-1019 (2021) https://doi.org/10.1177/1077546320936499
  4. Zhu, C., Tu, Q., Jiang, C., et al.: Global fast terminal sliding mode control strategy for permanent magnet synchronous motor based on load torque Luenberger observer. IEICE Electron. Express 18(19), 20210348-20210348 (2021) https://doi.org/10.1587/elex.18.20210348
  5. Yu, J., Chen, B., Yu, H., et al.: Position tracking control for chaotic permanent magnet synchronous motors via indirect adaptive neural approximation. Neurocomputing 156, 245-251 (2015) https://doi.org/10.1016/j.neucom.2014.12.054
  6. Li, S., Won, H., Fu, X., et al.: Neural-network vector controller for permanent-magnet synchronous motor drives: simulated and hardware-validated results. IEEE Trans. Cybern. 50(7), 3218-3230 (2019) https://doi.org/10.1109/TCYB.2019.2897653
  7. Cao, X., Ye, Y., Duan, Y., et al.: Sensorless control of permanent magnet synchronous motor based on adaptive back-EMF observer. Adv. Mech. Eng. 15(2), 16878132231151622 (2023)
  8. Liu, M., Wu, J., Sun, Y.: Fixed-time stability analysis of permanent magnet synchronous motors with novel adaptive control. Math. Probl. Eng.Probl. Eng. 2017, 1-11 (2017)
  9. Benevieri, A., Carbone, L., Cosso, S., et al.: Surface permanent magnet synchronous motors' passive sensorless control: a review. Energies 15(20), 7747 (2022)
  10. Salah, N., Samira, B., Moreau, S.: Modified backstepping control of IPMSM: experimental tests. Proc. Inst. Mech. Eng., Part I: J. Syst. Control Eng. 236(8), 1590-1602 (2022)
  11. Xue, G., Lin, F., Qin, B.: Adaptive neural network control of chaotic fractional-order permanent magnet synchronous motors using backstepping technique. Front. Phys. 8, 106 (2020)
  12. Wang, X., Chen, Y., Lu, Y., et al.: Dynamic surface method- based adaptive backstepping control for the permanent magnet synchronous motor on parameter identification. Proc. Inst. Mech. Eng., Part I: J. Syst. Control Eng. 233(9), 1172-1181 (2019)
  13. Luo, R., Deng, Y., Xie, Y.: Neural network backstepping controller design for uncertain permanent magnet synchronous motor drive chaotic systems via command filter. Front. Phys. 8, 182 (2020)
  14. Jiajun, W., Guangzhou, Z., Donglian, Qi.: Application of backstepping control in speed tracking control of permanent magnet synchronous motor Chinese. J. Electr. Eng. 24(8), 95-98 (2004)
  15. Ali, N., Alam, W., Pervaiz, M., et al.: Nonlinear adaptive backstepping control of permanent magnet synchronous motor. Rev. Roum. Sci. Tech. Ser. Electrotech. Energ. 66(1), 15-20 (2021)
  16. Zhang, Y., Yan, Q., Huang, N., et al.: Fuzzy approximation-based backstepping control of permanent magnet synchronous motor. J. Electr. Eng. Technol. 18(3), 2115-2126 (2023) https://doi.org/10.1007/s42835-022-01315-9
  17. Sun, X., Yu, H., Yu, J., et al.: Design and implementation of a novel adaptive backstepping control scheme for a PMSM with unknown load torque. IET Electr. Power Appl. 13(4), 445-455 (2019) https://doi.org/10.1049/iet-epa.2018.5656
  18. El-Sousy, F.F.M., El-Naggar, M.F., Amin, M., et al.: Robust adaptive neural-network backstepping control design for high-speed permanent-magnet synchronous motor drives: theory and experiments. IEEE Access 7, 99327-99348 (2019) https://doi.org/10.1109/ACCESS.2019.2930237
  19. Nguyen, T.H., Nguyen, T.T., Le, K.M., et al.: 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 (2023) https://doi.org/10.1109/ACCESS.2023.3248604