Multi-Objective Parameter Optimization-Based Design of Six-Pole Radial Hybrid Magnetic Bearing

Abstract

To reduce the couplings between the radial suspension forces and control currents caused by the asymmetry structure of the three-pole hybrid magnetic bearing (HMB) which is driven by an inverter, a six-pole radial HMB is proposed. To improve the bearing capacity and reduce the eddy current loss and volume of the six-pole radial HMB, an improved multi-objective particle swarm optimization (MOPSO) algorithm is proposed to optimize the parameters. At first, the structure and working principle of the six-pole radial HMB are introduced based on the equivalent magnetic circuit method. Then, the optimization objectives and optimization variables are determined and the number of multi-objective optimization parameters is reduced by sensitivity analysis. Next, the fitting function of the suspension force is obtained by the response surface method (RSM). Based on the theoretical analysis of the MOPSO, the linear decreasing inertia weight and the concept of hybridization are introduced to improve the diversity and convergence of the MOPSO to get Pareto optimal sets. Finally, simulation analysis and prototype experiment are carried out on the optimized six-pole radial HMB. The feasibility of the optimization method is verified by the results of simulation and experiment.