Journal of Power Electronics

  • 제24권7호
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  • Pages.1081-1094
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  • 2024
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  • 1598-2092(pISSN)
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  • 2093-4718(eISSN)
전력전자학회 (The Korean Institute of Power Electronics)
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Predictive field-oriented control of three-phase permanent magnet linear synchronous actuators

  • Mohammad Erfanimatin (School of Mechanical Engineering, College of Engineering, University of Tehran) ;
  • Suorena Saeedi (School of Mechanical Engineering, College of Engineering, University of Tehran) ;
  • Ali Sadighi (School of Mechanical Engineering, College of Engineering, University of Tehran)
  • 투고 : 2023.09.28
  • 심사 : 2024.02.21
  • 발행 : 2024.07.20
⟨ 이전 논문 다음 논문 ⟩

초록

Three-phase linear synchronous actuators play a pivotal role in precision motion control applications such as lithography machines and laser material processing stages. Achieving superior tracking performance is paramount in the design of motion control systems, prompting the utilization of advanced control algorithms. In this study, a novel approach is presented to enhance the tracking capability of a three-phase permanent magnet actuator by introducing a predictive field-oriented control system. The primary contribution of this paper lies in the comprehensive design and implementation of the predictive field-oriented control system. Initially, actuator modeling is conducted in the rotating reference frame (d-q frame) and finite element analysis is performed to determine key electrical quantities, including magnetic flux densities and inductances. To address the challenges posed by time-varying sinusoidal electrical signals, a field-oriented control methodology is proposed. Notably, the novelty of this work is underscored by a distinct emphasis on the predictive control strategy employed in the system. The predictive controller is implemented on a 32-bit ARM Cortex microcontroller, showcasing the practical viability of the proposed approach. Experimental results substantiate the effectiveness of the proposed method in achieving precise trajectory tracking. This paper contributes to the field by providing a rigorous analysis of a three-phase permanent magnet actuator and introducing a predictive field-oriented control system. The methodology outlined here enhances tracking capabilities and signifies a substantial advancement in the broader landscape of precision motion control systems. Thus, this work adds valuable insights to the existing body of knowledge in the domain, while offering a notable contribution to the field.

키워드

참고문헌

  1. Reed, D.M., Sun, J., Hofmann, H.F.: Simultaneous identification and adaptive torque control of permanent magnet synchronous machines. IEEE Trans. Control Syst. Technol. 25(4), 1372-1383 (2017). https://doi.org/10.1109/TCST.2016.2606351
  2. Zhang, F., Yin, H., Zhang, H.: Design and analysis of novel synchronous motion technique for a multi-module permanent magnet linear synchronous motor. Energies (Basel) (2022). https://doi.org/10.3390/en15103617
  3. Bai, C., Yin, Z., Zhang, Y., Liu, J.: Multiple-models adaptive disturbance observer-based predictive control for linear permanent-magnet synchronous motor vector drive. IEEE Trans. Power Electron. 37(8), 9596-9611 (2022). https://doi.org/10.1109/TPEL.2022.3155458
  4. Gonzalez, O., et al.: Model predictive current control of six-phase induction motor drives using virtual vectors and space vector modulation. IEEE Trans. Power Electron. 37(7), 7617-7628 (2022). https://doi.org/10.1109/TPEL.2022.3141405
  5. Li, X., Xue, Z., Zhang, L., Hua, W.: A low-complexity three-vector-based model predictive torque control for SPMSM. IEEE Trans. Power Electron. 36(11), 13002-13012 (2021). https://doi.org/10.1109/TPEL.2021.3079147
  6. Liu, X., Cao, H., Wei, W., Wu, J., Li, B., Huang, Y.: A practical precision control method base on linear extended state observer and friction feedforward of permanent magnet linear synchronous motor. IEEE Access 8, 68226-68238 (2020). https://doi.org/10.1109/ACCESS.2020.2986711
  7. Shu, S., Zhang, Y., Wang, X., Pan, Q., Tao, X.: Space vector control of a permanent magnet linear synchronous motor based on the improved single neuron PID Algorithm. J. Control Eng. Appl. Inform. 22(3), 74-84 (2020)
  8. Wai, R.J., Muthusamy, R.: Fuzzy-neural-network inherited sliding-mode control for robot manipulator including actuator dynamics. IEEE Trans. Neural. Netw. Learn. Syst. 24(2), 274-284 (2013). https://doi.org/10.1109/TNNLS.2012.2228230
  9. Yuan, H., Zhao, X., Fu, D.: Intelligent adaptive jerk control with dynamic compensation gain for permanent magnet linear synchronous motor servo system. IEEE Access 8, 138456-138469 (2020). https://doi.org/10.1109/ACCESS.2020.3012088
  10. Zhang, D., Chen, Y., Ai, W., Zhou, Z.: Precision motion control of permanent magnet linear motors. Int. J. Adv. Manuf. Technol. 35(3-4), 301-308 (2007). https://doi.org/10.1007/s00170-006-0727-8
  11. Li, Z., Zhang, Q., An, J., Xiao, Y., Sun, H.: Terminal sliding mode speed control method of permanent magnet synchronous linear motor based on adaptive parameter identification. Adv. Mech. Eng. (2021). https://doi.org/10.1177/16878140211012901
  12. Zhao, X., Fu, D.: Adaptive neural network nonsingular fast terminal sliding mode control for permanent magnet linear synchronous motor. IEEE Access 7, 180361-180372 (2019). https://doi.org/10.1109/ACCESS.2019.2958569
  13. El-Sousy, F.F.M., Abuhasel, K.A.: Nonlinear robust optimal control via adaptive dynamic programming of permanent-magnet linear synchronous motor drive for uncertain two-axis motion control system. IEEE Trans. Ind. Appl. 56(2), 1940-1952 (2020). https://doi.org/10.1109/TIA.2019.2961637
  14. Fu, D., Zhao, X., Zhu, J.: A novel robust super-twisting nonsingular terminal sliding mode controller for permanent magnet linear synchronous motors. IEEE Trans. Power Electron. 37(3), 2936-2945 (2022) https://doi.org/10.1109/TPEL.2021.3119029
  15. Ding, R., Ding, C., Xu, Y., Yang, X.: Neural-network-based adaptive robust precision motion control of linear motors with asymptotic tracking performance. Nonlinear Dyn. 108(2), 1339-1356 (2022). https://doi.org/10.1007/s11071-022-07258-0
  16. Wang, P., Xu, Y., Ding, R., Liu, W., Shu, S., Yang, X.: Multi-kernel neural network sliding mode control for permanent magnet linear synchronous motors. IEEE Access 9, 57385-57392 (2021). https://doi.org/10.1109/ACCESS.2021.3072958
  17. Wang, Y., Yu, H., Che, Z., Wang, Y., Zeng, C.: Extended state observer-based predictive speed control for permanent magnet linear synchronous motor. Processes (2019). https://doi.org/10.3390/pr7090618
  18. Jin, H.Y., Zhao, X.M.: Extended Kalman filter-based disturbance feed-forward compensation considering varying mass in high-speed permanent magnet linear synchronous motor. Electr. Eng. 101(2), 537-544 (2019). https://doi.org/10.1007/s00202-019-00802-z
  19. Sun, X., Wu, M., Yin, C., Wang, S.: Model predictive thrust force control for linear motor actuator used in active suspension. IEEE Trans. Energy Convers. 36(4), 3063-3072 (2021). https://doi.org/10.1109/TEC.2021.3069843
  20. Zhang, X., Zhao, Z.: Multi-stage series model predictive control for PMSM drives. IEEE Trans. Veh. Technol. 70(7), 6591-6600 (2021). https://doi.org/10.1109/TVT.2021.3086532
  21. Niu, S., Luo, Y., Fu, W., Zhang, X.: Robust model predictive control for a three-phase PMSM motor with improved control precision. IEEE Trans. Industr. Electron. 68(1), 838-849 (2021). https://doi.org/10.1109/TIE.2020.3013753
  22. Babes, B., Rahmani, L., Chaoui, A., Hamouda, N.: Design and experimental validation of a digital predictive controller for variable-speed wind turbine systems. J. Power Electron. 17(1), 232-241 (2017). https://doi.org/10.6113/JPE.2017.17.1.232
  23. Hamouda, N., et al.: Type-2 fuzzy logic predictive control of a grid connected wind power systems with integrated active power filter capabilities. J. Power Electron. 17(6), 1587-1599 (2017). https://doi.org/10.6113/JPE.2017.17.6.1587
  24. Hamouda, N., Babes, B., Kahla, S., Souf, Y., Petzoldt, J., Ellinger, T.: Predictive control of a grid connected PV system incorporating active power filter functionalities. In: 2019 1st International Conference on Sustainable Renewable Energy Systems and Applications (ICSRESA), 2019; pp. 1-6. https://doi.org/10.1109/ICSRESA49121.2019.9182655
  25. Aissa, O., Moulahoum, S., Colak, I., Babes, B., Kabache, N.: Analysis and experimental evaluation of shunt active power filter for power quality improvement based on predictive direct power control. Environ. Sci. Pollut. Res. 25(25), 24548-24560 (2018). https://doi.org/10.1007/s11356-017-0396-1
  26. Astrom, K.J., Wittenmark, B.: Adaptive control. in Addison-Wesley series in electrical and computer engineering: Control engineering. Addison-Wesley, 1989. [Online]. Available: https://books.google.de/books?id=VJ0eAQAAIAAJ. Accessed 22 Mar 2024