Abstract
In this paper, the modified Particle Swarm Optimization (PSO) algorithm is implemented to tune the gain parameters of the PI speed controller of the PMSM drive system. The PSO is one of the artificial intelligence techniques which is modified with the inertia weight updating mechanism to prevent premature convergence and balance the exploration and exploitation of the particles. The field-oriented vector control PMSM drive is developed in MATLAB/Simulink to examine three different conditions such as start-up, speed command change, and sudden load torque imposition. The different parameters are then examined such as speed overshoot, settling time, peak time, rise time and speed ripple and the results are compared with conventional PSO-tuned PI controllers for the same motor. From the results, it is proved that the modified PSO-PI controller gives better performance compared to the conventional PSO-PI speed controller.
Similar content being viewed by others
References
Wang M. A novel mathematical nonlinear PMSM realization method for electric machine emulator. IEEE J Emerg Sel Top Power Electron. 2022;10(4):4171–81. https://doi.org/10.1109/JESTPE.2022.3152429.
Iwama K, Noguchi T. High-efficiency drive method of adjustable field IPMSM utilizing magnetic saturation. IEEE Access. 2022;10:125499–508. https://doi.org/10.1109/ACCESS.2022.3226335.
Wu J, Wang J, Gan C, Sun Q, Kong W. Efficiency optimization of PMSM drives using field-circuit coupled FEM for EV/HEV applications. IEEE Access. 2018;6:15192–201. https://doi.org/10.1109/ACCESS.2018.2813987.
Wang Z, Chen J, Cheng M, Chau KT. Field-oriented control and direct torque control for paralleled VSIs fed PMSM drives with variable switching frequencies. IEEE Trans Power Electron. 2016;31(3):2417–28. https://doi.org/10.1109/TPEL.2015.2437893.
Ullah Z, Kim K-T, Park J-K, Hur J. Comparative analysis of scalar and vector control drives of IPMSM under inter-turn fault condition considering nonlinearities. In: 2015 IEEE energy conversion congress and exposition (ECCE). 2015; IEEE, p. 366–72.
Filho CJV, Xiao D, Vieira RP, Emadi A. Observers for high-speed sensorless PMSM drives: design methods, tuning challenges and future trends. IEEE Access. 2021;9:56397–415. https://doi.org/10.1109/ACCESS.2021.3072360.
Hussain HA. Tuning and performance evaluation of 2DOF PI current controllers for PMSM drives. IEEE Trans Transp Electrif. 2021;7(3):1401–14. https://doi.org/10.1109/TTE.2020.3043853.
Mehta ND, Haque AM, Makwana MV. Modeling and simulation of P, PI and PID controller for speed control of DC Motor Drive. Int J Sci Eng Res. 2017;8(7):556–62.
Jiang X, Wang Y, Dong J. Speed regulation method using genetic algorithm for dual three-phase permanent magnet synchronous motors. CES Trans Electr Mach Syst. 2023;7(2):171–8. https://doi.org/10.30941/CESTEMS.2023.00013.
Fan Y, Chen J, Zhang Q, Cheng M. An improved inertia disturbance suppression method for PMSM based on disturbance observer and two-degree-of-freedom PI controller. IEEE Trans Power Electron. 2023;38(3):3590–9. https://doi.org/10.1109/TPEL.2022.3218842.
Kumar A, Khan YA, Verma V. Comparative evaluation of PSO, TLBO, JAYA, whale optimization, and grey Wolf optimization based tuning of PI controllers for vector controlled synchronous reluctance motor drive. In: 2022 IEEE IAS global conference on emerging technologies (GlobConET). 2022; IEEE. p. 261–66.
Gandhi R, Wilson R, Kumar A, Roy R. Comparative analysis of vector controlled PMSM drive with particle swarm optimization and ant colony optimization technique. In: 2020 international conference on computational performance evaluation (ComPE). 2020; IEEE, p. 744–50.
Lai C, et al. PMSM drive system efficiency optimization using a modified gradient descent algorithm with discretized search space. IEEE Trans Transp Electrif. 2020;6(3):1104–14. https://doi.org/10.1109/TTE.2020.3004463.
Fodorean D, Idoumghar L, Brévilliers M, Minciunescu P, Irimia C. Hybrid differential evolution algorithm employed for the optimum design of a high-speed PMSM used for EV propulsion. IEEE Trans Ind Electron. 2017;64(12):9824–33. https://doi.org/10.1109/TIE.2017.2701788.
Song J, Zheng WX, Niu Y. Self-triggered sliding mode control for networked PMSM speed regulation system: a PSO-optimized super-twisting algorithm. IEEE Trans Ind Electron. 2022;69(1):763–73. https://doi.org/10.1109/TIE.2021.3050348.
Wilson R, Gandhi R, Kumar A, Roy R. Optimized vector control strategy for dual-rotor axial flux permanent magnet synchronous motor for in-wheel electric drive applications. In: 2020 international conference on computational performance evaluation (ComPE). 2020; IEEE, p. 676–81.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no known conflict of interest.
Ethical Approval
This article does not contain any studies with animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the topical collection “SWOT to AI-embraced Communication Systems (SWOT-AI)” guest edited by Somnath Mukhopadhyay, Debashis De, Sunita Sarkar and Celia Shahnaz.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gandhi, R., Bhattacharya, D., Anand, A. et al. Speed Control of PMSM Using Modified Particle Swarm Optimization Technique Based on Inertia Weight Updating Mechanism. SN COMPUT. SCI. 4, 774 (2023). https://doi.org/10.1007/s42979-023-02095-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-023-02095-3