Optimum Design of Homopolar Radial Two-Degree-of-Freedom Hybrid Magnetic Bearing

Abstract

Optimization design is a satisfactory way to improve the performance of magnetic bearing (MB). In this paper, a multi-objective genetic algorithm of particle swarm optimization (GAPSO) is proposed for homopolar permanent magnet biased magnetic bearings (HPRMBs). By assigning different inertia weights to each objective function, the multi-objective function is transformed into a new single objective function for optimization. In order to ensure the diversity of particles in the optimization process, genetic algorithm is used to cross-mutate them, which enhances the global search ability of particle swarm optimization. After optimization with GAPSO, the levitating force of the MB is increased by 22.3%, the volume decreased by 26.6%, and the loss reduced by 33.9%. The optimization results show that the multi-objective optimization based on GAPSO can effectively improve the performance of HPRMB.