volume | PIER Journals

Abstract

Aiming at the unsatisfactory accuracy and speed of traditional parameter identification methods for permanent magnet synchronous motors (PMSM), a parameter identification method based on an improved hunter prey optimization (HPO) algorithm (Tent chaotic initialization and firefly algorithm HPO (TF-HPO)) was proposed. Using the Tent chaotic map, the initial individuals are evenly distributed to enrich their diversity, and the population position is updated using the firefly perturbation algorithm. Simulation and practical experiments show that compared with unmodified algorithm, the improved algorithm has faster convergence speed and higher recognition accuracy, and can effectively identify the parameters of the motor. On this basis, deadbeat predictive current control is implemented, effectively eliminating current static errors and improving the accuracy and stability of the current control system, and can effectively suppress motor torque ripple and current harmonics caused by parameter deviations.

1. Wang, B., X. Chen, Y. Yu, et al. "Robust predictive current control with online disturbance estimation for induction machine drives," IEEE Transactions on Power Electronics, Vol. 32, No. 6, 4663-4674, 2016.
doi:10.1109/TPEL.2016.2602853 Google Scholar

2. Wang, Y., X. Wang, W. Xie, et al. "Deadbeat model-predictive torque control with discrete space-vector modulation for PMSM drives," IEEE Transactions on Industrial Electronics, Vol. 64, No. 5, 3537-3547, 2017.
doi:10.1109/TIE.2017.2652338 Google Scholar

3. Wang, B., X. Zhang, and H. B. Gooi, "An SI-MISO boost converter with deadbeat-based control for electric vehicle applications," IEEE Transactions on Vehicular Technology, Vol. 67, No. 10, 9223-9232, 2018.
doi:10.1109/TVT.2018.2853738 Google Scholar

4. Wang, B., U. Manandhar, X. Zhang, et al. "Deadbeat control for hybrid energy storage systems in DC microgrids," IEEE Transactions on Sustainable Energy, Vol. 10, No. 4, 1867-1877, 2018.
doi:10.1109/TSTE.2018.2873801 Google Scholar

5. Wang, P., Y. Bi, F. Gao, et al. "An improved deadbeat control method for single-phase PWM rectifiers in charging system for EVs," IEEE Transactions on Vehicular Technology, Vol. 68, No. 10, 9672-9681, 2019.
doi:10.1109/TVT.2019.2937653 Google Scholar

6. Saeed, M. S. R., W. Song, B. Yu, et al. "Low-complexity deadbeat model predictive current control with duty ratio for five-phase PMSM drives," IEEE Transactions on Power Electronics, Vol. 35, No. 11, 12085-12099, 2020.
doi:10.1109/TPEL.2020.2983048 Google Scholar

7. Cao, Y., R. Mao, L. Feng, et al. "Multiparameter identification of permanent magnet synchronous motors based on improved sparrow search algorithm," New Technology of Electrical Energy, Vol. 41, No. 05, 26-34, 2022. Google Scholar

8. Li, Y., X. Dong, H. Wei, et al. "Parameter identification of permanent magnet synchronous motors based on improved model reference adaptive systems," Control Theory and Application, Vol. 37, No. 09, 1983-1988, 2020. Google Scholar

9. Li, W. and Z. Du, "Parameter identification of permanent magnet synchronous motor based on improved grey wolf optimization algorithm," Modular Machine Tool and Automatic Machining Technology, Vol. 557, No. 07, 113-117, 2020. Google Scholar

10. Wu, Z., D. Yu, and X. Kang, "Parameter identification of solar cell models based on improved ant lion optimization algorithm," Acta Energiae Solaris Sinica, Vol. 40, No. 12, 3435-3443, 2019. Google Scholar

11. Liu, X., W. Hu, Y. Zou, et al. "Multi parameter identification of permanent magnet synchronous motors using improved particle swarm optimization," Journal of Electrical Machinery and Control, Vol. 24, No. 07, 112-120, 2020. Google Scholar

12. Turker, T., U. Buyukkeles, and A. F. Bakan, "A robust predictive current controller for PMSM drives," IEEE Transactions on Industrial Electronics, Vol. 63, No. 6, 3906-3914, 2016.
doi:10.1109/TIE.2016.2521338 Google Scholar

13. Wang, Z., A. Yu, X. Li, G. Zhang, and C. Xia, "A novel current predictive control based on fuzzy algorithm for PMSM," IEEE Journal of Emerging and Selected Topics in Power Electronics, 990-1001, 2019.
doi:10.1109/JESTPE.2019.2902634 Google Scholar

14. Siami, M., D. A. Khaburi, M. Rivera, and J. Rodriguez, "An experimental evaluation of predictive current control and predictive torque control for a PMSM fed by a matrix converter," IEEE Transactions on Industrial Electronics, Vol. 64, No. 11, 8459-8471, 2018.
doi:10.1109/TIE.2017.2703658 Google Scholar

15. El-Sousy, F. F. M., "Hybrid-based wavelet-neural-network tracking control for permanent-magnet synchronous motor servo drives," IEEE Transactions on Industrial Electronics, Vol. 57, No. 9, 3157-3166, 2010.
doi:10.1109/TIE.2009.2038331 Google Scholar

16. Wang, Y., X. Wang, W. Xie, et al. "Deadbeat model-predictive torque control with discrete space-vector modulation for PMSM drives," IEEE Transactions on Industrial Electronics, Vol. 64, No. 5, 3537-3547, 2017.
doi:10.1109/TIE.2017.2652338 Google Scholar

17. Wu, C., Y. Zhao, and M. Sun, "Multi-parameter online identification of permanent magnet synchronous motors using measured voltages," Proceedings of the CSEE, Vol. 40, No. 13, 4329-4340, 2020. Google Scholar

18. Wang, G., Current predictive control and current static error elimination algorithm for permanent magnet AC servo systems, Harbin Institute of Technology, 2014.

19. Naruei, I., F. Keynia, and A. Sabbagh Molahosseini, "Hunter-prey optimization: Algorithm and applications," Soft Computing, Vol. 26, 1279-1314, 2022.
doi:10.1007/s00500-021-06401-0 Google Scholar

20. Xue, J., Research and application of a new swarm intelligence optimization technology, Donghua University, 2020.

Mr. Chengmin Wang

Dr. Aiyuan Wang