volume | PIER Journals

Abstract

The goal of vector control of interior permanent magnet synchronous motor (IPMSM) is to make IPMSM have excellent dynamic and steady-state performance, but there is coupling between the d-q axis in the synchronous rotating coordinate system, which affects the torque response performance. In view of the fact that the traditional voltage compensation strategy is sensitive to the change of motor parameters, genetic algorithm is introduced to identify the parameters, and a feedforward voltage compensation control based on genetic algorithm parameter identification is proposed. The compensation voltage is calculated by the inductance and flux value of the motor identified by genetic algorithm. Compensation voltage is used to counteract the change of feedback voltage caused by the change of motor parameters in feedforward decoupling control. Simulated and experimental results show that the proposed strategy can effectively achieve d-q axis current decoupling, improve the dynamic performance of the system, and have excellent robustness.

1. Han, Z.-X. and J.-L. Liu, "Comparative analysis of vibration and noise in IPMSM considering the effect of MTPA control algorithms for electric vehicles," IEEE Transactions on Power Electronics, Vol. 36, No. 6, 6850-6862, 2021.
doi:10.1109/TPEL.2020.3036402 Google Scholar

2. Murakami, M., S. Morimoto, Y. Inoue, et al. "Maximum torque per ampere control of an IPMSM with magnetic saturation using online parameter identification," Proceedings of 2020 23rd International Conference on Electrical Machines and Systems, 1631-1636, Hamamatsu, Japan, 2020.
doi:10.23919/ICEMS50442.2020.9290907 Google Scholar

3. Fang, J.-C., Y.-Z. He, and Z.-Y. Wang, "Decoupling control strategy for high speed permanent magnet synchronous motor based on inversion system method," 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications, 895-898, Ottawa, Canada, 2014. Google Scholar

4. Guo, J., T. Fan, Q. Li, and e al., "Coupling and digital control delays affected stability analysis of permanent magnet synchronous motor current loop control," Vehicle Power and Propulsion Conference, 1-5, 2019. Google Scholar

5. Ban, F., G. Gu, and G. Lian, "Research on decoupling model predictive torque control strategy with load feedforward compensation for PMSM," 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devicesbuzai, 1-2, Tianjin, China, 2020. Google Scholar

6. Xiong, T., G. Zhou, and D.-D. Zou, "State feedback decoupling control of web tension velocity and lateral displacement in unwinding system," Chinese Control and Decision Conference, 5217-5224, 2020. Google Scholar

7. Chen, S.-Z., J.-F. Jiang, X.-H. Hou, et al. "Feedback linearized sliding mode control of PMSM based on a novel reaching law," Electrical Machines and Systems International Conference, 1438-1441, 2020. Google Scholar

8. Thieli, S. G., H. A. Grundling, and R. P. Vieira, "Sliding mode current control based on disturbance observer applied to permanent magnet synchronous motor," Proceedings of 1st Southern Power Electronics Conference, 1-6, Fortaleza, Brazil, 2015. Google Scholar

9. Scalcon, F. P., T. S. Gabbi, R. P. Vieira, et al. "Decoupled vector control based on disturbance observer applied to the synchronous reluctance motor," Proceedings of 21st European Conference on Power Electronics and Applications, P.1-P.8, Genova, Italy, 2019. Google Scholar

10. Gan, X.-Y., C. Liu, Y. Zuo, et al. "Analysis and dynamic decoupling control schemes for PMSM current loop," IEEE International Conference on Aircraft Utility Systems, 570-574, Beijing, China, 2016. Google Scholar

11. Lee, K. and J. Ha, "Dynamic decoupling control method for PMSM drive with cross-coupling inductances," IEEE Applied Power Electronics Conference and Exposition, 563-569, Tampa, FL, USA, 2017. Google Scholar

12. Jie, H., G. Zheng, J. Zou, et al. "Adaptive decoupling control using radial basis function neural network for permanent magnet synchronous motor considering uncertain and time-varying parameters," IEEE Access, Vol. 8, 112323-112332, 2020.
doi:10.1109/ACCESS.2020.2993648 Google Scholar

13. Calvini, M., M. Carpita, A. Formentini, et al. "PSO-based self-commissioning of electrical motor drives," IEEE Transactions on Industrial Electronics, Vol. 62, No. 2, 768-776, 2015.
doi:10.1109/TIE.2014.2349478 Google Scholar

14. Liu, Z., H.-L. Wei, Q.-C. Zhong, et al. "Parameter estimation for VSI-Fed PMSM based on a dynamic PSO with learning strategies," IEEE Transactions on Power Electronics, Vol. 32, No. 4, 3154-3165, 2017.
doi:10.1109/TPEL.2016.2572186 Google Scholar

15. Avdeev, A. and O. Osipov, "PMSM identification using genetic algorithm," International Workshop on Electric Drives: Improvement in Efficiency of Electric Drives, 1-4, Moscow, Russia, 2019. Google Scholar

16. Song, Z., Y. Lin, X. Mei, et al. "A novel inertia identification method for servo system using genetic algorithm," International Conference on Smart Grid and Electrical Automation, 22-25, Zhangjiajie, China, 2016. Google Scholar

Dr. Yanfei Pan

Dr. Xin Liu

Dr. Yilin Zhu

Dr. Bo Liu

Dr. Zhongshu Li