Robust Multi-Objective Optimization of a 3-Pole Active Magnetic Bearing Based on Combined Curves With Climbing Algorithm
Zhijia Jin, Xiaodong Sun, Long Chen, Zebin Yang
Abstract
With the extensive application of active magnetic bearings (AMBs), the robust multiobjective optimization of the structure seems to be a priority. However, it is a challenge due to the high dimension and huge computational cost of finite-element analysis. In this article, a robust multi-objective optimization method is proposed to pursue good performance for a three-pole AMB. To increase the efficiency of the optimization process, the Kendall correlation coefficient is applied to assist in determining the sensitivity. The parameters are divided into three layers, and a three-level multiobjective optimization structure is established. Meanwhile, Kriging model is employed to improve the optimization efficiency. The selection of the final solution in Pareto curves is always an issue. The proposed optimization structure can only ensure the performance of the AMB, rather than robustness. Thus, a robust solution selection method is proposed based on the climbing algorithm. The robustness can be easily shown in the Pareto curve obtained through the optimization structure. The final solution is selected with good robustness in terms of suspension force and force ripple. The experimental results based on a prototype are provided to verify the effectiveness of the proposed optimization method.