Volume 22, Issue 3 (JIAEEE Vol.22 No.3 2025)                   Journal of Iranian Association of Electrical and Electronics Engineers 2025, 22(3): 115-126 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Salmanfar M, Alizadeh Pahlavani M R, Dehestani Kolagar A, Koohmaskan Y. Position Control of a BLDC Permanent Magnet Synchronous Motor by Model Predictive Control Using Laguerre Functions and Particle Swarm Optimization Algorithm. Journal of Iranian Association of Electrical and Electronics Engineers 2025; 22 (3) :115-126
URL: http://jiaeee.com/article-1-1655-en.html
Position Control of a BLDC Permanent Magnet Synchronous Motor by Model Predictive Control Using Laguerre Functions and Particle Swarm Optimization Algorithm
Mojtaba Salmanfar , Mohammad Reza Alizadeh Pahlavani , Arash Dehestani Kolagar * , Yousef Koohmaskan
Faculty of Electrical & Computer Engineering, Malek Ashtar University of Technology
Abstract:   (1612 Views)
One of the factors that has an important effect on improving the efficiency of permanent magnet synchronous motors is the optimal design of the controller. One of the controllers that can be used to achieve optimal dynamics is the predictive controller. One of the most important challenges in the implementation of the predictive controller that limits its application is the high computational burden of this control method and the adjustment of the controller parameters. That is, in plants where the sampling time is small and the system dynamics are complex, correspondingly, the computational burden increases exponentially and the setting of the controller parameters becomes more complicated. This will reduce the speed of real-time implementation of this method on the one hand, and on the other hand, the adoption of a shorter prediction horizon. In this article, the main contribution is that in order to achieve a longer prediction horizon, the high computational volume is approximated by a number of discrete orthogonal basis functions, such as the Laguerre polynomials, and the control parameters are optimally adjusted by the particle swarm algorithm. The main advantage of this approach is to optimize the coefficients with a smaller number of orthogonal functions instead of optimizing the control path itself, which makes it possible to choose a longer prediction horizon.
Keywords: Permanent Magnet Synchronous Motor, Predictive Control, Laguerre Functions, Particle Swarm Optimization Algorithm
Full-Text [PDF 2488 kb]   (140 Downloads)    
Type of Article: Research | Subject: Power
Received: 2023/10/12 | Accepted: 2024/08/11 | Published: 2025/12/12
References
1. [1] Santanu. Mondal, Mitra. Arunabhaو and Madhurima Chattopadhyay. "Mathematical modeling and simulation of Brushless DC motor with ideal Back EMF for a precision speed control", 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2015. [DOI:10.1109/ICECCT.2015.7225944]
2. [2] Lad, Chetan K, and Rajagopalan Chudamani. "Simple overlap angle control strategy for commutation torque ripple minimisation in BLDC motor drive", IET Electric Power Applications 12.6 :797-807, 2018. [DOI:10.1049/iet-epa.2017.0644]
3. [3] Zhou, Xinxiu, et al. ",Rapid self-compensation method of commutation phase error for low-inductance BLDC motor", IEEE Transactions on Industrial Informatics 13.4 1833-1842, 2017. [DOI:10.1109/TII.2017.2653812]
4. [4] Kar, Manoj Kumar, et al. "Speed Control of a Brushless DC Motor Using Hall Sensor", International Conference on Electric Power and Renewable Energy. Singapore: Springer Nature Singapore, 2023.
5. [5] Wang, Yuxin, and Hong Wang. "Research on Control Technology of Brushless DC Motor for Robot", Proceedings of the 2024 8th International Conference on Control Engineering and Artificial Intelligence. 2024. [DOI:10.1145/3640824.3640854]
6. [6] Mondal, Santanu, Arunabha Mitra, and Madhurima Chattopadhyay. "Mathematical modeling and simulation of Brushless DC motor with ideal Back EMF for a precision speed control", 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2015. [DOI:10.1109/ICECCT.2015.7225944]
7. [7] Majhee, Akash Kumar, et al. "Performance Analysis and Simulation of Brushless DC Motor using PI with Hysteresis Current Controller", 2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC). IEEE, 2022. [DOI:10.1109/iSSSC56467.2022.10051496]
8. [8] Ketaki, Phatak, and M. R. Sindhu. "Model Predictive Control (MPC) Based BLDC Drive for Indian Drive Cycle", 2023 IEEE 8th International Conference for Convergence in Technology (I2CT). IEEE, 2023. [DOI:10.1109/I2CT57861.2023.10126284]
9. [9] Bokam Divakar, Dr RSR, et al. "A review on brushless Dc motor control techniques", Journal of Pharmaceutical Negative Results (2023): 6821-6828
10. [10] Kiyli, Selçuk, and Hasan şakir Bilge, "Modeling Brushless Direct Current Motor Of A Guided System", 2021 29th Signal Processing and Communications Applications Conference (SIU). IEEE, 2021. [DOI:10.1109/SIU53274.2021.9477923]
11. [11] Abdissa, Chala Merga. "Improved model predictive speed control of a PMSM via Laguerre functions", Mathematical Problems in Engineering 2024 (2024). [DOI:10.1155/2024/5562771]
12. [12] G. A. NeryJúnior, M. A. F. Martins, R. Kalid, "A PSO-based optimal tuning strategy for constrained multivariable predictive controllers with model uncertainty", ISA Transactions, vol. 53, no. 2, pp. 560-567, 2014. [DOI:10.1016/j.isatra.2013.12.019]
13. [13] J. E. Muralidhar, and P. V. Aranasi, "Torque ripple minimization & closed loop speed control of BLDC motor with hysteresis current controller", 2014 2nd International Conference on Devices, Circuits and Systems (ICDCS). IEEE, 2014. [DOI:10.1109/ICDCSyst.2014.6926122]
14. [14] tavakoli A, khorasani F. Improvement of speed control performance sensorless Brushless DC motor with finite control set model predictive control. Journal of Iranian Association of Electrical and Electronics Engineers 2023; 20 (2) :195-203 [DOI:10.52547/jiaeee.20.2.195]
15. [15] F. Tahami, M. Abedi. Analysis and Design of Predictive Control for Sheppard-Taylor Based PFC Rectifier . Journal of Iranian Association of Electrical and Electronics Engineers 2012; 8 (1) :21-29
16. [16] J. Rodriguez, M. P. Kazmierkowski, J. R. Espinoza, P. Zanchetta, H. Abu-Rub, H. A. Young, and C. A. Rojas, "State of the art of finite control set model predictive control in power electronics", IEEE Transactions on Industrial Informatics, vol. 9, no. 2, pp. 1003-1016, 2012. [DOI:10.1109/TII.2012.2221469]
17. [17] Y. Zhou, H. Li, R. Liu, and J. Mao, "Continuous voltage vector model free predictive current control of surface mounted permanent magnet synchronous motor", IEEE Transactions on Energy Conversion, vol. 34, no. 2, pp. 899-908, 2018. [DOI:10.1109/TEC.2018.2867218]
18. [18] Sáenz, F. González, and O. Sandre Hernández, "Model predictive current control of a permanent magnet synchronous machine with exponential cost function", 2021 18th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE). IEEE, 2021. [DOI:10.1109/CCE53527.2021.9633094]
19. [19] Gao, Lihua, et al. "A novel method of model predictive control on permanent magnet synchronous machine with Laguerre functions", Alexandria Engineering Journal 60.6 : 5485-5494, 2021 [DOI:10.1016/j.aej.2021.03.035]
20. [20] Ubare, Pramod, and D. N. Sonawane.، "Performance Assessment of the BLDC Motor in EV Drives using Nonlinear Model Predictive Control", Engineering, Technology & Applied Science Research 12.4: 8901-8909, 2022 [DOI:10.48084/etasr.4976]
21. [21] M. Dorfling, H. Mouton, T. Geyer, and P. Karamanakos,، "Long-horizon finite-control-set model predictive control with non-recursive sphere decoding on an FPGA", IEEE Trans. Power Electron, vol. 35, no. 7, pp. 7520-7531, Jul. 2020. [DOI:10.1109/TPEL.2019.2956213]
22. [22] P. Bagheri and A. Khaki-Sedigh, "A review of methods for tuning predictive controllers and novel tuning approaches," Journal of Control, vol. 8, no. 3, p. 6, Autumn 2014, ISSN 2008-8345.
23. [23] K. Chen, H. Chen, C. Zhou et al., "Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data", Water Research, vol. 171, Article ID 115454, 2020. ation of key water parameters using different machine learning models based on big data", Water Research, vol. 171, Article ID 115454, 2020. [DOI:10.1016/j.watres.2019.115454]
Add your comments about this article : Your username or Email:
CAPTCHA
Send email to the article author

Rights and permissions
Creative Commons License This Journal is an open access Journal Licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. (CC BY NC 4.0)