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

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

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

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

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

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.

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

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

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.

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

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

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

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

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

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.

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.

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

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.

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

Prof. Huangqiu Zhu

Dr. Mingcan Wu