A model predictive control method for permanent magnet synchronous motor based on parameter identification and dead time compensation is proposed to solve the problems of poor parameter robustness and large current errors. In this method, the prediction model is firstly established based on the mathematical model of the permanent magnet synchronous motor. After that, the current error caused by the parameter change in the prediction model and the current harmonics caused by the dead time effect are basically analyzed theoretically. Then, the adaptive linear neural network algorithm is proposed to identify the motor parameters and applied to the prediction model, and the harmonic components are filtered out using the adaptive linear neural network algorithm. The recursive least squares algorithm is used to quickly update the system weights to improve the dead time compensation control effect. Finally, the effectiveness and correctness of the proposed algorithm are verified on the experimental platform. The experimental results show that the predictive control method of permanent magnet synchronous motor model based on parameter identification and dead time compensation can effectively reduce the current error of the control system and accelerate the dynamic response of the speed.
2. Huang, Y., T. Tao, Y. Liu, K. Chen, and F. Yang, "DSC-FLL based sensorless control for permanent magnet synchronous motor," Progress In Electromagnetics Research M, Vol. 98, 171-181, 2020.
3. Zhu, L., B. Xu, and H. Zhu, "Interior permanent magnet synchronous motor dead-time compensation combined with extended Kalman and neural network bandpass filter," Progress In Electromagnetics Research M, Vol. 98, 193-203, 2020.
4. Liu, X., Y. Pan, Y. Zhu, H. Han, and L. 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.
5. Zhu, Y., Y. Bai, H. Wang, and L. Sun, "Sensorless control of permanent magnet synchronous motor based on T-S fuzzy inference algorithm fractional order sliding mode," Progress In Electromagnetics Research M, Vol. 105, 161-172, 2021.
6. Fan, M., H. Lin, and T. Lan, "Model predictive direct torque control for spmsm with load angle limitation," Progress In Electromagnetics Research B, Vol. 58, 245-256, 2014.
7. Wang, Z., A. Yu, X. Li, G. Zhang, and C. Xia, "A novel current predictive control based on fuzzy algorithm for PMSM," IEEE Journal of Emerging and Selected Topics in Power Electronics, Vol. 7, No. 2, 990-1001, Jun. 2019.
8. Zhang, X., L. Zhang, and Y. Zhang, "Model predictive current control for PMSM drives with parameter robustness improvement," IEEE Transactions on Power Electronics, Vol. 34, No. 2, 1645-1657, Feb. 2019.
9. Niu, S., Y. Luo, W. Fu, and X. Zhang, "Robust model predictive control for a three-phase PMSM motor with improved control precision," IEEE Transactions on Industrial Electronics, Vol. 68, No. 1, 838-849, Jan. 2021.
10. Zhang, Y., J. Jin, and L. Huang, "Model-free predictive current control of PMSM drives based on extended state observer using ultralocal model," IEEE Transactions on Industrial Electronics, Vol. 68, No. 2, 993-1003, Feb. 2021.
11. Li, X., W. Tian, X. Gao, Q. Yang, and R. Kennel, "A generalized observer-based robust predictive current control strategy for PMSM drive system," IEEE Transactions on Industrial Electronics, Vol. 69, No. 2, 1322-1332, Feb. 2022.
12. Gao, M., H. Zhu, and Y. Shi, "Predictive direct control of permanent magnet assisted bearingless synchronous reluctance motor based on super twisting sliding mode," Progress In Electromagnetics Research M, Vol. 102, 105-115, 2021.
13. Zhu, H. and M. Wu, "Direct control of bearingless permanent magnet synchronous motor based on prediction model," Progress In Electromagnetics Research M, Vol. 101, 127-138, 2021.
14. Ji, Y., Y. Yong, J Zhou, H. Ding, X. Guo, and S. Padmanaban, "Control strategies of mitigating dead-time effect on power converters: An overview," Electronics, Vol. 8, No. 2, 196, 2019.