Improved Model Predictive Current Control for PMSM Motor With Consensus Algorithm
VERSION OF RECORD ONLINE: 17/09/2025
Corressponding author's email:
pmduc@hcmut.edu.vnDOI:
https://doi.org/10.54644/jte.2025.1807Keywords:
Permanent Magnet Synchronous Motor (PMSM), Model Predictive Control (MPC), Consensus Algorithm, Extended Kalman Filter, Cost Function OptimizationAbstract
The use of PMSM drives is becoming more prominent in industrial applications due to their benefits of high torque and power density, outstanding efficiency, and excellent reliability. Because of these benefits, various speed control methods for PMSMs have been researched and developed, with each method having its own advantages and limitations. Among these methods, Model predictive control (MPC) is a highly efficient control strategy for permanent magnet synchronous motors (PMSMs) that can manage constrained multi-objective optimization while delivering excellent dynamic performance. However, the accuracy of traditional predictive current control models relies heavily on motor parameters such as inductance and resistance. Additionally, the values of factors in the cost function are often inconsistent, as they are typically determined based on experience. This study introduces a solution to overcome the mentioned drawbacks by integrating MPC with a consensus algorithm and an extended Kalman filter. The consensus algorithm updates the weight values in the cost function for optimal performance, while the extended Kalman filter estimates the motor inductance and resistance, ensuring accurate control for MPC. This improved combined method makes motor speed control more adaptive to changes in resistance and inductance during operation. It ensures a system response with minimal overshoot, reduced steady-state error, and quicker settling time. The simulation results of the scale-down PMSM system are obtained from the Matlab simulation platform.
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References
Z. Qiao, T. Shi, Y. Wang, Y. Yan, C. Xia, and X. He, “New sliding-mode observer for position sensorless control of permanent-magnet synchronous motor,” IEEE Transactions on Industrial Electronics, vol. 60, no. 2, pp. 710–719, 2013, doi: 10.1109/TIE.2012.2206359.
W. Xu, S. Qu, L. Zhao, and H. Zhang, “An Improved Adaptive Sliding Mode Observer for Middle- And High-Speed Rotor Tracking,” IEEE Trans Power Electron, vol. 36, no. 1, pp. 1043–1053, Dec. 2021, doi: 10.1109/TPEL.2020.3000785.
T. Li, X. Sun, M. Yao, D. Guo, and Y. Sun, “Improved finite control set model predictive current control for permanent magnet synchronous motor with sliding mode observer,” IEEE Transactions on Transportation Electrification, 2023.
Z. Jin, X. Sun, G. Lei, Y. Guo, and J. Zhu, “Sliding mode direct torque control of SPMSMs based on a hybrid wolf optimization algorithm,” IEEE Transactions on Industrial Electronics, vol. 69, no. 5, pp. 4534–4544, 2021.
Y. Zhang, 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, pp. 993–1003, 2020.
T. T. Nguyen, H. N. Tran, T. H. Nguyen, and J. W. Jeon, “Recurrent neural network-based robust adaptive model predictive speed control for PMSM with parameter mismatch,” IEEE Transactions on Industrial Electronics, vol. 70, no. 6, pp. 6219–6228, 2022.
Y. Gao, L. Dai, Y. Xia, and Y. Liu, “Distributed model predictive control for consensus of nonlinear second-order multi-agent systems,” International Journal of Robust and Nonlinear Control, vol. 27, no. 5, pp. 830–842, Mar. 2017, doi: 10.1002/rnc.3603.
M. Hirche, P. N. Kohler, M. A. Muller, and F. Allgower, “Distributed Model Predictive Control for Consensus of Constrained Heterogeneous Linear Systems,” in Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 1248–1253. doi: 10.1109/CDC42340.2020.9303838.
M. Thoma and M. Morari, “Lecture Notes in Control and Information Sciences 374.”
PRECEDE 2019: 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE): proceedings: Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Quanzhou, China, May 31 - June 2, 2019. IEEE, 2019.
X. Zhang, L. Zhang, and Y. Zhang, “Model predictive current control for PMSM drives with parameter robustness improvement,” IEEE Trans Power Electron, vol. 34, no. 2, pp. 1645–1657, Feb. 2019, doi: 10.1109/TPEL.2018.2835835.
I. F. Bouguenna, A. Tahour, R. Kennel, and M. Abdelrahem, “Multiple-vector model predictive control with fuzzy logic for PMSM electric drive systems,” Energies (Basel), vol. 14, no. 6, Mar. 2021, doi: 10.3390/en14061727.
P. Wang, X. Yuan, and C. Zhang, “An Improved Model Free Predictive Current Control for PMSM with Current Prediction Error Variations,” IEEE Access, vol. 10, pp. 54537–54548, 2022, doi: 10.1109/ACCESS.2022.3175501.
D. Yuan, S. Xu, and H. Zhao, “Distributed primal-dual subgradient method for multiagent optimization via consensus algorithms,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 41, no. 6, pp. 1715–1724, Dec. 2011, doi: 10.1109/TSMCB.2011.2160394.
H. J. Yoo, T. T. Nguyen, and H. M. Kim, “Consensus-based distributed coordination control of hybrid AC/DC microgrids,” IEEE Trans Sustain Energy, vol. 11, no. 2, pp. 629–639, Apr. 2020, doi: 10.1109/TSTE.2019.2899119.
H. J. Yoo, T. T. Nguyen, and H. M. Kim, “Consensus-based distributed coordination control of hybrid AC/DC microgrids,” IEEE Trans Sustain Energy, vol. 11, no. 2, pp. 629–639, Apr. 2020, doi: 10.1109/TSTE.2019.2899119.
M. L. Jayaramu, H. N. Suresh, M. S. Bhaskar, D. Almakhles, S. Padmanaban, and U. Subramaniam, “Real-time implementation of extended kalman filter observer with improved speed estimation for sensorless control,” IEEE Access, vol. 9, pp. 50452–50465, 2021.
E. Espina, R. Cardenas-Dobson, J. W. Simpson-Porco, D. Saez, and M. Kazerani, “A Consensus-Based Secondary Control Strategy for Hybrid AC/DC Microgrids with Experimental Validation,” IEEE Trans Power Electron, vol. 36, no. 5, pp. 5971–5984, May 2021, doi: 10.1109/TPEL.2020.3031539.
R. O. Saber, J. A. Fax, and R. M. Murray, “Consensus and cooperation in networked multi-agent systems,” Proceedings of the IEEE, vol. 95, no. 1, pp. 215–233, Jan. 2007, doi: 10.1109/JPROC.2006.887293.
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