volume | PIER Journals

Abstract

Permanent magnet synchronous motor (PMSM) used in high-end applications has stringent control performance requirements. However, harsh environments, complex operating conditions, and nonlinear parameter variations can compromise model adaptability, which undermines system reliability and precision. This paper proposes a model-free adaptive control (MFAC) method that utilizes a Multi-Innovation Improved Extended Kalman Filter (MIIEKF) algorithm for prediction and update to enhance system reliability and accuracy. First, the proposed method transforms the PMSM model into a compact-form dynamic linearization (CFDL) data model, which mitigates the need for precise mathematical modeling. Next, an improved Extended Kalman Filter (IEKF) algorithm is used to predict and update the pseudo partial derivative (PPD) in real-time. This resolves its estimation dependency and compensates for data model inaccuracies. Then, the IEKF algorithm is optimized by using Multi-Innovation identification theory to ensure rapid state convergence. Finally, experimental validation confirms that the proposed method significantly improves the convergence rate, reduces chattering, and achieves efficient data-driven control compared to PI control and conventional model-free adaptive control.

1. Orlowska-Kowalska, Teresa, Marcin Wolkiewicz, Przemyslaw Pietrzak, Maciej Skowron, Pawel Ewert, Grzegorz Tarchala, Mateusz Krzysztofiak, and Czeslaw T. Kowalski, "Fault diagnosis and fault-tolerant control of PMSM drives–state of the art and future challenges," IEEE Access, Vol. 10, 59979-60024, 2022.
doi:10.1109/access.2022.3180153 Google Scholar

2. Chen, Wenwen, Zhaoyong Mao, and Wenlong Tian, "Water cooling structure design and temperature field analysis of permanent magnet synchronous motor for underwater unmanned vehicle," Applied Thermal Engineering, Vol. 240, 122243, Mar. 2024.
doi:10.1016/j.applthermaleng.2023.122243 Google Scholar

3. Singh, Anant Kumar, Ramakrishnan Raja, Tomy Sebastian, and Awab Ali, "Limitations of the PI control with respect to parameter variation in PMSM motor drive systems," 2019 IEEE International Electric Machines & Drives Conference (IEMDC), 1688-1693, San Diego, CA, USA, May 12-15, 2019.
doi:10.1109/IEMDC.2019.8785406

4. Ke, Dongliang, Fengxiang Wang, Xinhong Yu, S. Alireza Davari, and Ralph Kennel, "Predictive error model-based enhanced observer for PMSM deadbeat control systems," IEEE Transactions on Industrial Electronics, Vol. 71, No. 3, 2242-2252, Mar. 2024.
doi:10.1109/tie.2023.3269476 Google Scholar

5. Yin, Zhenxiao, Xueyan Wang, Xiangdong Su, Yang Shen, Dianxun Xiao, and Hang Zhao, "A switched ultra-local model-free predictive controller for PMSMs," IEEE Transactions on Power Electronics, Vol. 39, No. 9, 10665-10669, Sep. 2024.
doi:10.1109/tpel.2024.3399599 Google Scholar

6. Davari, S. Alireza, Mahdi S. Mousavi, Behnam Nikmaram, Freddy Flores-Bahamonde, Fengxinag Wang, Patrick Wheeler, and Jose Rodriguez, "Sensorless model-free predictive control of permanent magnet synchronous motor," IEEE Transactions on Industrial Electronics, 1-12, 2025.
doi:10.1109/tie.2025.3589366 Google Scholar

7. Wang, Lanbing, Shuo Zhang, Chengning Zhang, Yu Huang, Yuelin Dong, and Shulin Wang, "An improved model-free adaptive current control for PMSM based on prior-optimization," IEEE Transactions on Industrial Electronics, Vol. 72, No. 6, 5591-5601, Jun. 2025.
doi:10.1109/tie.2024.3488276 Google Scholar

8. Chu, Wendi and Xinlong Shui, "Application of predictive control for synchronous motors in oil field automation," International Core Journal of Engineering, Vol. 11, No. 10, 56-65, 2025.
doi:10.6919/ICJE.202510_11(10).0008 Google Scholar

9. Hou, Zhongsheng, Ronghu Chi, and Huijun Gao, "An overview of dynamic-linearization-based data-driven control and applications," IEEE Transactions on Industrial Electronics, Vol. 64, No. 5, 4076-4090, 2017.
doi:10.1109/tie.2016.2636126 Google Scholar

10. Cao, Yuanchao, Xing Ren, Qing Guo, Guicheng Wu, Qiang Long, and Mengfan Ran, "Position sensorless control of PMSM based on EKF current noise compensation and MRAS," 2024 IEEE International Conference on Mechatronics and Automation (ICMA), 870-875, Tianjin, China, 2024.
doi:10.1109/ICMA61710.2024.10632984

11. Dan, Luo, "EKF-based fault detection and isolation for PMSM inverter," Sustainable Energy Technologies and Assessments, Vol. 52, 101846, Aug. 2022.
doi:10.1016/j.seta.2021.101846 Google Scholar

12. Ding, Feng, "Least squares parameter estimation and multi-innovation least squares methods for linear fitting problems from noisy data," Journal of Computational and Applied Mathematics, Vol. 426, 115107, Jul. 2023.
doi:10.1016/j.cam.2023.115107 Google Scholar

13. Hou, Zhongsheng and Shuangshuang Xiong, "On model-free adaptive control and its stability analysis," IEEE Transactions on Automatic Control, Vol. 64, No. 11, 4555-4569, Nov. 2019.
doi:10.1109/tac.2019.2894586 Google Scholar

14. Xiao, Jie, Yonglian Xiong, Yucheng Zhu, Chao Zhang, Ting Yi, Xing Qian, Yongsheng Fan, and Quanhui Hou, "Multi-innovation adaptive Kalman filter algorithm for estimating the SOC of lithium-ion batteries based on singular value decomposition and Schmidt orthogonal transformation," Energy, Vol. 312, 133597, Dec. 2024.
doi:10.1016/j.energy.2024.133597 Google Scholar

15. Lim, Steve, "Sensorless-FOC for PMSM with single DC-link shunt," 1-32, Application Note, Texas Instruments, 2020.

16. Vas, P., Sensorless Vector and Direct Torque Control, Oxford University Press, 1998.
doi:10.1093/oso/9780198564652.001.0001

17. Hou, Zhongsheng and Shangtai Jin, Model Free Adaptive Control, CRC Press, Boca Raton, FL, USA, 2013.
doi:10.1201/b15752

Professor Kaihui Zhao

Dr. Youzhuo Duan

Professor Jie Xiong

Dr. Lingxuan Tu

Dr. Yishan Huang