volume | PIER Journals

Abstract

In order to further reduce the computational complexity as well as the average switching frequency of the inverter for model predictive torque control (MPTC), an improved MPTC control strategy for a three-vector low switching frequency based permanent magnet synchronous motor is proposed. Firstly, an analysis is conducted on the combined effect of the torque and magnetic chain based on the three voltage vectors, based on which the vector combinations are matched to form an offline optimized switching table, and then the three voltage vector combinations are selected from the offline optimized switching table according to the torque control requirements in order to reduce the amount of system calculations. Then, on this basis, a hysteresis loop technique for direct torque control is introduced to reduce the average switching frequency of the inverter. An improved MPTC control strategy with fuzzy variable hysteresis loop width is further proposed to fuzzy control the dynamic output hysteresis loop width scaling factor according to the motor operating state. Experimental results show that the improved MPTC control strategy with fuzzy variable hysteresis loop width results in optimal combined average switching frequency and current harmonics with reduced computational effort.

1. Tong, W., S. Dai, S. Wu, and R. Tang, "Performance comparison between an amorphous metal PMSM and a silicon steel PMSM," Performance comparison between an amorphous metal PMSM and a silicon steel PMSM, Vol. 55, No. 6, 1-5, June 2019, Art No. 8102705, doi: 10.1109/TMAG.2019.2900531.

2. Sun, X., Z. Shi, G. Lei, Y. Guo, and J. Zhu, "Analysis and design optimization of a permanent magnet synchronous motor for a campus patrol electric vehicle," IEEE Transactions on Vehicular Technology, Vol. 68, No. 11, 10535-10544, Nov. 2019, doi: 10.1109/TVT.2019.2939794.
doi:10.1109/TVT.2019.2939794

3. Siami, I. M., D. A. Khaburi, A. Abbaszadeh, and J. Rodríguez, "Robustness improvement of predictive current control using prediction error correction for permanent-magnet synchronous machines," IEEE Transactions on Industrial Electronics, Vol. 63, No. 6, 3458-3466, June 2016, doi: 10.1109/TIE.2016.2521734.
doi:10.1109/TIE.2016.2521734

4. Zhao, G., J. Feng, and Q. Sun, "The research of optimized torque control algorithm for PMSM based on grey prediction model," 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 335-340, 2009, doi: 10.1109/FSKD.2009.588.
doi:10.1109/FSKD.2009.588

5. Chen, W. and D. Sun, "simplified robust model predictive flux control of open-winding PMSM based on ESO," 2019 22nd International Conference on Electrical Machines and Systems (ICEMS), 1-6, 2019, doi: 10.1109/ICEMS.2019.8921676.

6. Zhang, X., K. Yan, and M. Cheng, "Two-stage series model predictive torque control for PMSM drives," IEEE Transactions on Power Electronics, Vol. 36, No. 11, 12910-12918, Nov. 2021, doi: 10.1109/TPEL.2021.3075711.
doi:10.1109/TPEL.2021.3075711

7. Ji, J., R. Xue, W. Zhao, T. Tao, and L. Huang, "Simplified three-vector-based model predictive thrust force control with cascaded optimization process for a double-side linear vernier permanent magnet motor," IEEE Transactions on Power Electronics, Vol. 35, No. 10, 10681-10689, Oct. 2020, doi: 10.1109/TPEL.2020.2976901.
doi:10.1109/TPEL.2020.2976901

8. Sun, X., et al. "MPTC for PMSMs of EVs With multi-motor driven system considering optimal energy allocation," IEEE Transactions on Magnetics, Vol. 55, No. 7, 1-6, July 2019, Art No. 8104306, doi: 10.1109/TMAG.2019.2904289.
doi:10.1109/TMAG.2019.2904289

9. Chen, L., H. Xu, X. Sun, and Y. Cai, "Three-vector-based model predictive torque control for a permanent magnet synchronous motor of EVs," IEEE Transactions on Transportation Electrification, Vol. 7, No. 3, 1454-1465, Sept. 2021, doi: 10.1109/TTE.2021.3053256.
doi:10.1109/TTE.2021.3053256

10. Luo, Y. and C. Liu, "A flux constrained predictive control for a six-phase PMSM motor with lower complexity," IEEE Transactions on Industrial Electronics, Vol. 66, No. 7, 5081-5093, 2019.
doi:10.1109/TIE.2018.2868301

11. Huang, W., W. Hua, F. Yin, et al. "Model predictive thrust force control of a linear fluxswitching permanent magnet machine with voltage vectors selection and synthesis," IEEE Transactions on Industrial Electronics, Vol. 66, No. 6, 4956-4967, 2019.
doi:10.1109/TIE.2018.2835381

12. Lim, C. S., E. Levi, M. Jones, N. Abdul Rahim, and W. P. Hew, "Experimental evaluation of model predictive current control of a five-phase induction motor using all switching states," 2012 15th International Power Electronics and Motion Control Conference (EPE/PEMC), LS1c.4-1-LS1c.4-7, 2012, doi: 10.1109/EPEPEMC.2012.6397394.

13. Zhang, Y., B. Xia, and H. Yang, "Performance evaluation of an improved model predictive control with field oriented control as a benchmark," IET Electric Power Applications, Vol. 11, No. 5, 677-687, 2017.
doi:10.1049/iet-epa.2015.0614

14. Ang, Y. L., X. C. Wang, W. Xie, et al. "Deadbeat modelpredictive 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

15. Nasr, A., C. Gu, G. Buticchi, S. Bozhko, and C. Gerada, "A low-complexity modulated model pre-dictive torque and flux control strategy for PMSM drives without weighting factor," IEEE Journal of Emerging and Selected Topics in Power Electronics, doi: 10.1109/JESTPE.2022.3152652.

16. Guo, T., Z. Wang, H. Zhang, X. Jiang, and L. Tian, "Cascaded predictive speed control optimization method based on fuzzy controller," 2021 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 328-335, 2021, doi: 10.1109/PRECEDE51386.2021.9680980.
doi:10.1109/PRECEDE51386.2021.9680980

17. Xiang, C., X. Zhang, Z. Li, L. Zhang, and S. Cheng, "Model predict torque control of induction motor based on the DTC switching table," 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 1348-1352, 2020, doi: 10.1109/ICIEA48937.2020.9248310.
doi:10.1109/ICIEA48937.2020.9248310

18. Hou, L., J. Ma, and W. Wang, "Sliding mode predictive current control of permanent magnet synchronous motor with cascaded variable rate sliding mode speed controller," IEEE Access, Vol. 10, 33992-34002, 2022, doi: 10.1109/ACCESS.2022.3161629.
doi:10.1109/ACCESS.2022.3161629

19. Dan, H., P. Zeng, W. Xiong, M. Wen, M. Su, and M. Rivera, "Model predictive control-based direct torque control for matrix converter-fed induction motor with reduced torque ripple," CES Transactions on Electrical Machines and Systems, Vol. 5, No. 2, 90-99, June 2021, doi: 10.30941/CESTEMS.2021.00012.
doi:10.30941/CESTEMS.2021.00012

20. Geyer, T., Model Predictive Control of High Power Converters and Industrial Drives, John Wiley & Sons, Inc., Chichester, 2016.
doi:10.1002/9781119010883

21. Kim, H., J. Han, Y. Lee, J. Song, and K. Lee, "Torque predictive control of permanent-magnet synchronous motor using duty ratio prediction," 2013 IEEE International Symposium on Industrial Electronics, 1-5, 2013, doi: 10.1109/ISIE.2013.6563664.

22. Dragičvić, T. and M. Novak, "Weighting factor design in model predictive control of power electronic converters: An artificial neural network approach," IEEE Transactions on Industrial Electronics, Vol. 66, No. 11, 8870-8880, Nov. 2019, doi: 10.1109/TIE.2018.2875660.
doi:10.1109/TIE.2018.2875660

Dr. Qianghui Xiao

Dr. Zhe Li

Dr. Yang Zhang

Dr. Bing Luo

Dr. Tingting Wang