volume | PIER Journals

Abstract

The direct control for the bearingless permanent magnet synchronous motor (BPMSM) has problems of large ripples of flux linkage, torque, and suspension force due to sampling time delay. To solve above problems, a predictive direct control method is proposed based on the traditional direct control by adding prediction model. Firstly, the generation principle of radial suspension forces of the BPMSM is introduced. Secondly, the models of the predictive direct control method are given based on the traditional direct control, and the time-delay compensation model is deduced. Thirdly, the predictive direct control system is constructed, and the simulations are carried out. Finally, the proposed control strategy is applied to a prototype, and the related experimental results are given and analyzed. The results of the simulations and experiments show that compared with the traditional direct control of the BPMSM, the predictive direct control strategy can effectively reduce the ripples of flux linkage, torque, and suspension forces, and improve the static and dynamic performance of the BPMSM.

1. Zhu, H. and Z. Gu, "Active disturbance rejection control of 5-degree-of-freedom bearingless permanent magnet synchronous motor based on fuzzy neural network inverse system," ISA Transactions, Vol. 101, 295-308, Jun. 2020.
doi:10.1016/j.isatra.2020.01.028 Google Scholar

2. Zhao, C. and H. Zhu, "Design and analysis of a novel bearingless flux-switching permanent magnet motor," IEEE Transactions on Industrial Electronics, Vol. 64, No. 8, 6127-6136, Aug. 2017.
doi:10.1109/TIE.2017.2682018 Google Scholar

3. Sun, X., L. Chen, and Z. Yang, "Overview of bearingless permanent magnet synchronous motors," IEEE Transactions on Industrial Electronics, Vol. 60, No. 12, 5528-5538, Dec. 2013.
doi:10.1109/TIE.2012.2232253 Google Scholar

4. Zhang, S. and F. L. Luo, "Direct control of radial displacement for bearingless permanent-magnet-type synchronous motors," IEEE Transactions on Industrial Electronics, Vol. 56, No. 2, 542-552, Feb. 2009.
doi:10.1109/TIE.2008.2003219 Google Scholar

5. Takahashi, I. and T. Noguchi, "A new quick-response and high-efficiency control strategy of an induction motor," IEEE Transactions on Industry Applications, Vol. IA-22, No. 5, 820-827, Sept. 1986.
doi:10.1109/TIA.1986.4504799 Google Scholar

6. Zhu, H., et al. "Direct torque and direct suspension force control of bearingless permanent magnet synchronous motor," Proc. Int. Conf. Chinese Control and Decision Conference (CCDC), 2411-2415, Xuzhou, 2010. Google Scholar

7. Naik, N. V., A. Panda, and S. P. Singh, "A three-level fuzzy-2 DTC of induction motor drive using SVPWM," IEEE Transactions on Industrial Electronics, Vol. 63, No. 3, 1467-1479, Mar. 2016.
doi:10.1109/TIE.2015.2504551 Google Scholar

8. Iacchetti, M. F., G. D. Marques, and R. Perini, "Torque ripple reduction in a DFIG-DC system by resonant current controllers," IEEE Transactionson Power. Electronics, Vol. 30, No. 8, 4244-4254, Aug. 2015.
doi:10.1109/TPEL.2014.2360211 Google Scholar

9. Ammar, A., et al. "Predictive direct torque control with reduced ripples and fuzzy logic speed controller for induction motor drive," Proc. Int. Conf. International Conference on Electrical Engineering-Boumerdes (ICEE-B), 1-6, Boumerdes, 2017. Google Scholar

10. Ammar, A., B. Abdelhamid, and B. Amor, "Closed loop torque SVM-DTC based on robust super twisting speed controller for induction motor drive with efficiency optimization," International Journal of Hydrogen Energy, Vol. 42, No. 28, 17940-17952, Apr. 2017.
doi:10.1016/j.ijhydene.2017.04.034 Google Scholar

11. Wang, F., et al. "Model-based predictive direct control strategies for electrical drives: An experimental evaluation of PTC and PCC methods," IEEE Transactions on Industrial Informatics, Vol. 11, No. 3, 671-681, Jun. 2015.
doi:10.1109/TII.2015.2423154 Google Scholar

12. Correa, P., M. Pacas, and J. Rodriguez, "Predictive torque control for inverter-fed induction machines," IEEE Transactions on Industrial Electronics, Vol. 54, No. 2, 1073-1079, Apr. 2007.
doi:10.1109/TIE.2007.892628 Google Scholar

13. Li, C., et al. "An improved finite-state predictive torque control for switched reluctance motor drive," IET Electric Power Applications, Vol. 12, No. 1, 144-151, Jan. 2018.
doi:10.1049/iet-epa.2017.0268 Google Scholar

14. Xiao, M., et al. "Predictive torque control of permanent magnet synchronous motor using flux vector," IEEE Transactions on Industry Applications, Vol. 54, No. 5, 4437-4446, Sept.-Oct. 2018.
doi:10.1109/TIA.2018.2833817 Google Scholar

15. Cao, B., et al. "Direct torque model predictive control of a polyphase permanent magnet synchronous motor with current harmonic suppression and loss reduction," Proc. Int. Conf. IEEE Applied Power Electronics Conference and Exposition (APEC), 2460-2464, San Antonio, 2018. Google Scholar

16. Hanke, S., O. Wallscheid, and J. Bocker, "A direct model predictive torque control approach to meet torque and loss objectives simultaneously in permanent magnet synchronous motor applications," Proc. Int. Conf. Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 101-106, Pilsen, 2017. Google Scholar

17. Wang, T., et al. "Model predictive torque control of permanent magnet synchronous motors with extended set of voltage space vectors," IET Electric Power Applications, Vol. 11, No. 8, 1376-1382, Sept. 2017.
doi:10.1049/iet-epa.2016.0870 Google Scholar

18. Zhang, W., et al. "Direct control of bearingless permanent magnet slice motor based on active disturbance rejection control," IEEE Transactions on Applied Superconductivity, Vol. 30, No. 4, 1-5, Jun. 2020. Google Scholar

19. Sun, X., et al. "High-performance control for a bearingless permanent-magnet synchronous motor using neural network inverse scheme plus internal model controllers," IEEE Transactions on Industrial Electronics, Vol. 63, No. 6, 3479-3488, Jun. 2016.
doi:10.1109/TIE.2016.2530040 Google Scholar

Prof. Huangqiu Zhu

Dr. Mingcan Wu