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MULTI-OBJECTIVE OPTIMIZATION DESIGN OF MAGNETIC BEARING BASED ON GENETIC PARTICLE SWARM OPTIMIZATION

By Y. Sun, S. Yin, Y. Yuan, Y. Huang, and F. Yang

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Abstract:
The performance of magnetic bearing is determined by its electromagnetic parameters and mechanical parameters. In order to improve the performance of hybrid magnetic bearing (HMB) to better meet the engineering requirements, which needs to be optimized, a multi-objective optimization method based on genetic particle swarm optimization algorithm (GAPSO) is proposed in this paper to solve the problem that the optimization objectives are not coordinated during the optimization design. By introducing the working principle of HMB, a mathematical model of suspension force is established, and its rationality is verified by the finite-element method. By optimization, the suspension force of the HMB is increased by 18.5%, and the volume is reduced by 22%. The optimization results show that the multi-objective optimization algorithm based on GAPSO can effectively improve the performance of HMB.

Citation:
Y. Sun, S. Yin, Y. Yuan, Y. Huang, and F. Yang, "Multi-Objective Optimization Design of Magnetic Bearing Based on Genetic Particle Swarm Optimization," Progress In Electromagnetics Research M, Vol. 81, 181-192, 2019.
doi:10.2528/PIERM19031904

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