Optimization Design of Permanent Magnet Synchronous Motor Based on Multi-Objective Particle Swarm Optimization Algorithm

Xiaolong Zhang; Zimo Chai

doi:10.54097/ha7cfz25

Authors

Xiaolong Zhang
Zimo Chai

DOI:

https://doi.org/10.54097/ha7cfz25

Keywords:

PMSMs, Multi-Objective Particle Swarm Optimization, Response Surface Methodology

Abstract

Permanent Magnet Synchronous Motors (PMSMs) are characterized by high power density, strong overload capability, high efficiency, and compact size. To enhance the torque performance of PMSMs, an optimization design method combining response surface model with Multi-Objective Particle Swarm Optimization (MOPSO) is proposed. First, the main dimensions of the motor are determined based on the requirements of the motor, followed by the selection of the structural type. The initial structural model of the motor is established using finite element software. Subsequently, sensitivity analysis is performed based on key dimensional parameters. Based on the sensitivity levels of these parameters, a response surface model is developed for highly sensitive parameters, and a multi-objective genetic optimization algorithm is applied for further optimization. Finally, a comparative analysis of the torque performance between the optimized and initial motor is conducted, demonstrating the effectiveness and feasibility of the proposed method.

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References

[1] Boby R A. Kinematic identification of industrial robot using end-effector mounted monocular camera bypassing measurement of 3-D pose[J]. IEEE/ASME Transactions on Mechatronics, 2021, 27(1): 383-394.

[2] Li W, Wang Y, Togo S, et al. Development of a humanoid shoulder based on 3-motor 3 degrees-of-freedom coupled tendon-driven joint module[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1105-1111.

[3] Wen K, Nguyen T S, Harton D, et al. A backdrivable kinematically redundant (6+3)-degree-of-freedom hybrid parallel robot for intuitive sensorless physical human–robot interaction[J]. IEEE Transactions on Robotics, 2020, 37(4): 1222-1238.

[4] Pyo H J, Song S W, Nam D W, et al. Study on inductance parameter compensation method for flux–torque control of IPMSM[J]. IEEE Transactions on Magnetics, 2022, 58(8): 1-5.

[5] Sun X, Tang Y, Xiao X, et al. Predictive trajectory control strategy for permanent magnet synchronous motor drives based on deadbeat predictive flux linkage control method[J]. IEEE Transactions on Power Electronics, 2022, 38(2): 2327-2338.

[6] Wang Y, Qin Y, Chen X, et al. Absolute inductive angular displacement sensor for position detection of YRT turntable bearing[J]. IEEE Transactions on Industrial Electronics, 2022, 69(10): 10644-10655.

[7] Fan D, Quan L, Zhu X, et al. Airgap-harmonic-based multilevel design and optimization of a double-stator flux-modulated permanent-magnet motor[J]. IEEE Transactions on Industrial Electronics, 2020, 68(11): 10534-10545.

[8] Zhao W, Ma A, Ji J, et al. Multi-objective optimization of a double-side linear Vernier PM motor using response surface method and differential evolution[J]. IEEE Transactions on Industrial Electronics, 2019, 67(1): 80-90.