volume | PIER Journals

Abstract

In model predictive current control (MPCC), in order to reduce the switching frequency, the number of switching changes is introduced into the cost function. But it will lead to the complexity of weight coefficient adjustment. To solve the problem, a dual cost function model predictive control (DCF-MPC) strategy for permanent magnet synchronous motor (PMSM) is proposed. First, the dual cost function is established, and the cost function g₁ first screens out the combination of two or three voltage vectors which minimizes the current steady-state error. Then, the cost function g₂ selects the voltage vector combination that minimizes the number of switching changes from the selected voltage vector combinations in g₁ as the optimal voltage vector combination. Finally, the experiment shows that compared with the traditional single cost function, the proposed method eliminates the weight coefficient of MPCC, simplifies the system structure and reduces the amount of calculation. Moreover, it suppresses the stator current ripple, reduces the harmonic content of three-phase current, and has better steady-state and dynamic performance under different working conditions.

1. Yu, H., J. Wang, and Z. Xin, "Model predictive control for PMSM based on discrete space vector modulation with RLS parameter identification," Energies, Vol. 15, No. 11, 1-16, 2022.

2. Rodriguez, J., J. Pontt, C. A. Silva, et al. "Predictive current control of a voltage source inverter," IEEE Transactions on Industrial Electronics, Vol. 54, No. 1, 495-503, 2007.
doi:10.1109/TIE.2006.888802

3. Lyu, Z., X. Wu, J. Gao, et al. "An improved finite-control-set model predictive current control for IPMSM under model parameter mismatches," Energies, Vol. 14, No. 19, 1-13, 2021.
doi:10.3390/en14196342

4. Uddin, M. and M. Rahman, "Online torque-flux estimation-based nonlinear torque and flux control scheme of IPMSM drive for reduced torque ripples," IEEE Transactions on Power Electronics, Vol. 34, No. 1, 636-645, 2019.
doi:10.1109/TPEL.2018.2827332

5. Pak, S. and I. Kang, "Sensorless vector control of permanent magnetic synchronous motor with parameter error compensation," International Transactions on Electrical Energy Systems, Vol. 31, No. 5, e12787, 2021.
doi:10.1002/2050-7038.12787

6. Xia, C., J. Zhao, Y. Yan, et al. "A novel direct torque control of matrix converter fed PMSM drives using duty cycle control for torque ripple reduction," IEEE Transactions on Industrial Electronics, Vol. 61, No. 6, 2700-2713, 2013.
doi:10.1109/TIE.2013.2276039

7. Yao, X., C. Huang, J. Wang, et al. "Predictive current control of dual vector model of permanent magnet synchronous motor with parameter identification function," Proceedings of the CSEE, 1-13, 2022.

8. Zhang, Y., J. Zhu, and W. Xu, "Predictive torque control of permanent magnet synchronous motor drive with reduced switching frequency," 2010 International Conference on Electrical Machines and Systems, 798-803, 2010.

9. Yao, J., R. Liu, and X. Yin, "Research on predictive control of three-vector low switching frequency model of permanent magnet synchronous motor," Transactions of China Electrotechnical Society, Vol. 33, No. 13, 2935-2945, 2018.

10. Habibullah, M., D. Lu, D. Xiao, et al. "Predictive torque control of induction motor sensorless drive fed by a 3L-NPC inverter," IEEE Transactions on Industrial Informatics, Vol. 13, No. 1, 60-70, 2016.
doi:10.1109/TII.2016.2603922

11. Zhang, Z., Y. Liu, J. Chen, et al. "Predictive control strategy of permanent magnet synchronous motor amplitude control set model," Transactions of China Electrotechnical Society, Vol. 37, No. 23, 6126-6134, 2022.

12. Tu, W., G. Luo, and W. Liu, "Prediction of current control by finite control set model of permanent magnet synchronous motor based on fuzzy dynamic cost function," Transactions of China Electrotechnical Society, Vol. 32, No. 16, 89-97, 2017.

13. Li, J., F. Wang, D. Ke, et al. "Design of predictive control weight coefficient of permanent magnet synchronous motor model based on particle swarm algorithm," Transactions of China Electrotechnical Society, Vol. 36, No. 1, 50-59+76, 2021.

14. Novak, M., H. Xie, T. Dragicevic, et al. "Optimal cost function parameter design in predictive torque control (PTC) using artificial neural networks (ANN)," IEEE Transactions on Industrial Electronics, Vol. 68, No. 8, 7309-7319, 2021.
doi:10.1109/TIE.2020.3009607

15. Abbaszadeh, A., D. Khaburi, H. Mahmoudi, et al. "Simplified model predictive control with variable weighting factor for current ripple reduction," IET Power Electronics, Vol. 10, No. 10, 1165-1174, 2017.
doi:10.1049/iet-pel.2016.0483

16. Liu, X., D. Wang, and Z. Peng, "Cascade-free fuzzy finite-control-set model predictive control for nested neutral point-clamped converters with low switching frequency," IEEE Transactions on Control Systems Technology, Vol. 27, No. 5, 2237-2244, 2019.
doi:10.1109/TCST.2018.2839091

17. Guazzelli, P., W. de Andrade Pereira, C. de Oliveira, et al. "Weighting factors optimization of predictive torque control of induction motor by multi-objective genetic algorithm," IEEE Transactions on Power Electronics, Vol. 34, No. 7, 6628-6638, 2018.
doi:10.1109/TPEL.2018.2834304

18. Caseiro, L., A. Mendes, and S. Cruz, "Dynamically weighted optimal switching vector model predictive control of power converters," IEEE Transactions on Industrial Electronics, Vol. 66, No. 2, 1235-1245, 2019.
doi:10.1109/TIE.2018.2829689

19. Wang, F., J. Li, Z, Li, et al. "Design of model predictive control weighting factors for PMSM using gaussian distribution-based particle swarm optimization," IEEE Transactions on Industrial Electronics, Vol. 69, No. 11, 10935-10946, 2022.
doi:10.1109/TIE.2021.3120441

20. Rojas, C., J. Rodriguez, F. Villarroel, et al. "Predictive torque and flux control without weighting factors," IEEE Transactions on Industrial Electronics, Vol. 60, No. 2, 681-690, 2012.
doi:10.1109/TIE.2012.2206344

21. Xu, Y., Y. He, and S. Li, "Logical operation-based model predictive control for quasi-Z-source inverter without weighting factor," IEEE Journal of Emerging and Selected Topics in Power Electronics, Vol. 9, No. 1, 1039-1051, 2021.
doi:10.1109/JESTPE.2020.2973183

22. Zhang, Y. and H. Yang, "Two-vector-based model predictive torque control without weighting factors for induction motor drives," IEEE Transactions on Power Electronics, Vol. 31, No. 2, 1381-1390, 2016.
doi:10.1109/TPEL.2015.2416207

23. Zhang, Y., H. Yang, and B. Xia, "Model-predictive control of induction motor drives: Torque control versus flux control," IEEE Transactions on Industry Applications, Vol. 52, No. 5, 4050-4060, 2016.
doi:10.1109/TIA.2016.2582796

24. Wang, F., H. Xie, Q. Chen, et al. "Parallel predictive torque control for induction machines without weighting factors," IEEE Transactions on Power Electronics, Vol. 35, No. 2, 1779-1788, 2020.
doi:10.1109/TPEL.2019.2922312

25. Liu, J., Z. Ge, X. Wu, et al. "Predictive current control of permanent magnet synchronous motor based on duty cycle modulation," Proceedings of the CSEE, Vol. 40, No. 10, 3319-3328, 2020.

Dr. Dingdou Wen

Dr. Wenting Zhang

Dr. Zhe Li

Dr. Zhongjian Tang

Dr. Yang Zhang

Dr. Yun Ling