Vol. 110
Latest Volume
All Volumes
PIERM 130 [2024] PIERM 129 [2024] PIERM 128 [2024] PIERM 127 [2024] PIERM 126 [2024] PIERM 125 [2024] PIERM 124 [2024] PIERM 123 [2024] PIERM 122 [2023] PIERM 121 [2023] PIERM 120 [2023] PIERM 119 [2023] PIERM 118 [2023] PIERM 117 [2023] PIERM 116 [2023] PIERM 115 [2023] PIERM 114 [2022] PIERM 113 [2022] PIERM 112 [2022] PIERM 111 [2022] PIERM 110 [2022] PIERM 109 [2022] PIERM 108 [2022] PIERM 107 [2022] PIERM 106 [2021] PIERM 105 [2021] PIERM 104 [2021] PIERM 103 [2021] PIERM 102 [2021] PIERM 101 [2021] PIERM 100 [2021] PIERM 99 [2021] PIERM 98 [2020] PIERM 97 [2020] PIERM 96 [2020] PIERM 95 [2020] PIERM 94 [2020] PIERM 93 [2020] PIERM 92 [2020] PIERM 91 [2020] PIERM 90 [2020] PIERM 89 [2020] PIERM 88 [2020] PIERM 87 [2019] PIERM 86 [2019] PIERM 85 [2019] PIERM 84 [2019] PIERM 83 [2019] PIERM 82 [2019] PIERM 81 [2019] PIERM 80 [2019] PIERM 79 [2019] PIERM 78 [2019] PIERM 77 [2019] PIERM 76 [2018] PIERM 75 [2018] PIERM 74 [2018] PIERM 73 [2018] PIERM 72 [2018] PIERM 71 [2018] PIERM 70 [2018] PIERM 69 [2018] PIERM 68 [2018] PIERM 67 [2018] PIERM 66 [2018] PIERM 65 [2018] PIERM 64 [2018] PIERM 63 [2018] PIERM 62 [2017] PIERM 61 [2017] PIERM 60 [2017] PIERM 59 [2017] PIERM 58 [2017] PIERM 57 [2017] PIERM 56 [2017] PIERM 55 [2017] PIERM 54 [2017] PIERM 53 [2017] PIERM 52 [2016] PIERM 51 [2016] PIERM 50 [2016] PIERM 49 [2016] PIERM 48 [2016] PIERM 47 [2016] PIERM 46 [2016] PIERM 45 [2016] PIERM 44 [2015] PIERM 43 [2015] PIERM 42 [2015] PIERM 41 [2015] PIERM 40 [2014] PIERM 39 [2014] PIERM 38 [2014] PIERM 37 [2014] PIERM 36 [2014] PIERM 35 [2014] PIERM 34 [2014] PIERM 33 [2013] PIERM 32 [2013] PIERM 31 [2013] PIERM 30 [2013] PIERM 29 [2013] PIERM 28 [2013] PIERM 27 [2012] PIERM 26 [2012] PIERM 25 [2012] PIERM 24 [2012] PIERM 23 [2012] PIERM 22 [2012] PIERM 21 [2011] PIERM 20 [2011] PIERM 19 [2011] PIERM 18 [2011] PIERM 17 [2011] PIERM 16 [2011] PIERM 14 [2010] PIERM 13 [2010] PIERM 12 [2010] PIERM 11 [2010] PIERM 10 [2009] PIERM 9 [2009] PIERM 8 [2009] PIERM 7 [2009] PIERM 6 [2009] PIERM 5 [2008] PIERM 4 [2008] PIERM 3 [2008] PIERM 2 [2008] PIERM 1 [2008]
2022-06-06
Torque Compensation Method of Switched Reluctance Motor Adopting MPC Based on TSF-DITC
By
Progress In Electromagnetics Research M, Vol. 110, 211-221, 2022
Abstract
Aiming at the problem of large torque ripple caused by large tracking error between actual torque and reference torque in commutation region in direct instantaneous torque control (DITC) algorithm of switched reluctance motor (SRM) based on torque sharing function (TSF), a torque compensation method combining TSF-DITC and model predictive control (MPC) is proposed. Sectors are subdivided in the commutation region according to the rotor position. Different voltage states are selected in different sectors to fully compensate for the tracking error between the actual phase torque and the reference torque distributed by TSF, and then the total torque ripple is greatly reduced. At the same time, the algorithm also effectively reduces the candidate voltage states at the current time and reduces the computational burden. The simulation comparison with TSF-DITC shows that the algorithm (TSF-PDITC) has better steady-state and dynamic performance.
Citation
Yang Yang, Aide Xu, Bing Leng, Jinghao Sun, and Kuo Li, "Torque Compensation Method of Switched Reluctance Motor Adopting MPC Based on TSF-DITC," Progress In Electromagnetics Research M, Vol. 110, 211-221, 2022.
doi:10.2528/PIERM22040803
References

1. Bilgin, B., B. Howey, A. D. Callegaro, et al. "Making the case for switched reluctance motors for propulsion applications," IEEE Transactions on Vehicular Technology, Vol. 69, No. 7, 7172-7186, 2020.
doi:10.1109/TVT.2020.2993725

2. Zan, X., Z. Jiang, K. Ni, et al. "Modular battery management for SRM drives in hybrid vehicles based on a novel modular converter," IEEE Access, Vol. 8, No. 1, 136296-136306, 2020.
doi:10.1109/ACCESS.2020.3011451

3. Aiso, K. and A. Kan, "High speed SRM using vector control for electric vehicle," Transactions on Electrical Machines and Systems, Vol. 4, No. 1, 61-68, 2020.
doi:10.30941/CESTEMS.2020.00009

4. Sun, Q., J. Wu, C. Gan, et al. "Multi-level converter-based torque sharing function control strategy for switched reluctance motors," International Conference on Electrical Machines Systems, 1-5, IEEE, 2017.

5. Liu, Y., L. I. Jie, and C. Shan, "Direct instantaneous torque control of switched reluctance motor based on optimal angle adaptive TSF," Journal of Beijing University of Aeronautics and Astronautics, Vol. 45, No. 11, 2152-2159, 2019.

6. Li, Z. and Z. Kan, "A high efficiency direct instantaneous torque control of SRM," Transactions of China Electro technical Society, Vol. 25, No. 8, 31-37, 2010.

7. Valenciagarcia, D. F., R. Tarvirdilu-Asl, C. Garcia, et al. "A review of predictive control techniques for switched reluctance machine drives," IEEE Transactions on Energy Conversion, Vol. 36, No. 2, 1323-1335, 2020.
doi:10.1109/TEC.2020.3047981

8. Valenciagarcia, D. F., R. Tarvirdilu-Asl, C. Garcia, et al. "A review of predictive control techniques for switched reluctance machine drives. Part II: Torque control, assessment and challenges," IEEE Transactions on Energy Conversion, Vol. 36, No. 2, 1323-1335, 2020.
doi:10.1109/TEC.2020.3047981

9. Elmorshedy, M. F., W. Xu, F. El-Sousy, et al. "Recent achievements in model predictive control techniques for industrial motor: A comprehensive state-of-the-art," IEEE Access, Vol. 9, No. 1, 58170-58191, 2021.
doi:10.1109/ACCESS.2021.3073020

10. Shang, C., A. Xu, L. Huang, et al. "Flux linkage optimization for direct torque control of switched reluctance motor based on model predictive control," IEEE Transactions on Electrical and Electronic Engineering, Vol. 14, No. 7, 1105-1113, 2019.
doi:10.1002/tee.22906

11. Hu, K., L. Guo, and J. Ye, "Model predictive current control of mutually coupled switched reluctance machines using a three-phase voltage source converter," IEEE Applied Power Electronics Conference and Exposition (APEC), 704-710, New Orleans, 2020.
doi:10.1109/APEC39645.2020.9124115

12. Le-Huy, H. and P. Brunelle, "A versatile nonlinear switched reluctance motor model in Simulink using realistic and analytical magnetization characteristics," Industrial Electronics Society, 6, 2005.