Vol. 103
Latest Volume
All Volumes
Decoupling Control of Permanent Magnet Synchronous Motor Based on Parameter Identification of Fuzzy Least Square Method
Progress In Electromagnetics Research M, Vol. 103, 49-60, 2021
In order to improve the performance of decoupling control for an interior permanent magnet synchronous motor (IPMSM), a recursive least square algorithm with fuzzy forgetting factor is proposed to identify IPMSM parameters. Firstly, the problems of coupling and parameter identification of IPMSM are analyzed. Secondly, the identification process of resistance and flux linkage is analyzed, and the static parameters are identified as the initial value or constant value. Thirdly, fuzzy control is used to dynamically adjust the forgetting factor in the recursive least square algorithm to make the identification of direct axis and quadrature axis inductance parameters more accurate. Finally, the effectiveness and accuracy of the proposed parameter identification algorithm are verified on the platform, and the good performance of the proposed algorithm in decoupling control is verified. The experimental results show that the identification method can accurately identify the motor parameters in static state and dynamic state. At the same time, the forgetting factor is dynamically adjusted to improve the parameter identification effect and decoupling control performance of the motor.
Xin Liu, Yanfei Pan, Yilin Zhu, Hui Han, and Lei Ji, "Decoupling Control of Permanent Magnet Synchronous Motor Based on Parameter Identification of Fuzzy Least Square Method," Progress In Electromagnetics Research M, Vol. 103, 49-60, 2021.

1. Yin, S. and W. Wang, "Study on the flux-weakening capability of permanent magnet synchronous motor for electric vehicle," Mechatronics, Vol. 38, 115-120, 2016.

2. Liu, G., G. Qiu, S. Jin, and F. G. Zhang, "Study on counter-rotating dual-rotor permanent magnet motor for underwater vehicle propulsion," IEEE Transactions on Applied Superconductivity, Vol. 28, No. 3, 1-5, 2018.

3. Zhang, B., Q. Li, G. Feng, B. Wang, and H. Sun, "Study on mine hoist driven by PMSM of low voltage and multi-branch," Advanced Materials Research, Vol. 2140, 22-25, 2013.

4. Knypinski, L. and J. Krupinski, "The slewing drive system for tower crane with permanent magnet synchronous motor," Archives of Electrical Engineering, Vol. 70, No. 1, 189-201, 2021.

5. Aubert, B., J. Regnier, S. Caux, D. Alejo, and , "Kalman-filter-based indicator for online interturn short circuits detection in permanent-magnet synchronous generators," IEEE Transactions on Industrial Electronics, Vol. 62, No. 3, 1921-1930, 2015.

6. She, Z., J. Liu, Q. Liang, and W. Zou, "Identification for PMSM rotor speed based on optimized extended kalman filter and load torque observer," 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 1-2, Tianjin, China, 2020.

7. Shi, Y., "Online identification of permanent magnet flux based on extended kalman filter for IPMSM drive with position sensorless control," IEEE Transactions on Industrial Electronics, Vol. 59, No. 11, 4169-4178, 2012.

8. Cheng, L., X. J. Ye, D. R. Sun, Y. Ye, and Y. Jin, "Low speed compound direct-drive permanent magnet synchronous motor control system with load torque compensation," Applied Mechanics & Materials, Vol. 416–417, 652-657, 2013.

9. Zhong, C. and Y. Lin, "Model reference adaptive control (MRAC)-based parameter identification applied to surface-mounted permanent magnet synchronous motor," International Journal of Electronics, Vol. 104, No. 11, 1854-1873, 2017.

10. Qu, Z. Y. and Z. M. Ye, "Speed regulation of a permanent magnet synchronous motor via model reference adaptive control," Advanced Materials Research, Vol. 268–270, 513-516, 2011.

11. Kesavan, P. and A. Karthikeyan, "Electromagnetic torque-based model reference adaptive system speed estimator for sensorless surface mount permanent magnet synchronous motor drive," IEEE Transactions on Industrial Electronics, Vol. 67, No. 7, 5936-5947, 2020.

12. Aliprantis, D. C., S. D. Sudhoff, and B. T. Kuhn, "Genetic algorithm-based parameter identification of a hysteretic brushless exciter model," IEEE Transactions on Energy Conversion, Vol. 21, No. 1, 148-154, 2006.

13. Gaur, P., B. Singh, and A. Mittal, "Artificial neural network based controller and speed estimation of permanent magnet synchronous motor," 2008 Joint International Conference on Power System Technology and IEEE Power India Conference, 1-6, New Delhi, India, 2008.

14. Sandre-Hernandez, O., R. Morales-Caporal, J. Rangel-Magdaleno, and H. Peregrina-Barreto, "Parameter identification of PMSMs using experimental measurements and a PSO algorithm," IEEE Transactions on Instrumentation & Measurement, Vol. 64, No. 8, 2146-2154, 2015.

15. Liu, Z. H., H. L. Wei, X. H. Li, and K. Liu, "Global identification of electrical and mechanical parameters in PMSM drive based on dynamic self-learning PSO," IEEE Transactions on Power Electronics, Vol. 33, No. 12, 10858-10871, 2018.

16. Li, Y., B. Zhang, and X. Xu, "Decoupling control for permanent magnet in-wheel motor using internal model control based on back-propagation neural network inverse system," Bulletin of the Polish Academy of Sciences: Technical Sciences, Vol. 66, No. 6, 1-12, 2018.

17. Zhang, J. L. and C. S. Zhang, "Parameters identification of induction motor for electric vehicle based on least squares method," Advanced Materials Research, Vol. 383–390, 648-653, 2011.

18. Zhou, Y., H. Wang, and J. Lian, "Research on online parameter identification of interior permanent magnet synchronous motor based on augmented robust forgetting factor recursive least square," Transactions on Emerging Telecommunications Technologies, Vol. 31, No. 12, 1-13, 2020.

19. Leopold, S., F. Maurice, P. Maria, and P. Guillaume, "MTPV flux-weakening strategy for PMSM high speed drive," IEEE Transactions on Industry Applications, Vol. 54, No. 6, 6081-6089, 2018.