基于粒子群优化算法主动悬架作动器多目标优化设计

发布时间：2018-04-29 11:56

本文选题：电磁作动器 + 气隙磁场　；参考：《中南大学学报(自然科学版)》2017年04期

【摘要】：为了使电动轮主动悬架系统的电磁直线作动器具有高推力密度、低铜耗和低推力波动特性,提出基于粒子群算法的圆筒形Halbach永磁直线同步电机作动器多目标优化设计方法,推导Halbach直线作动器径向气隙磁场密度、空载感应电动势、电磁力解析式,并采用有限元法对其进行了验证。基于Halbach磁体结构的气隙磁场进行参数化分析,获得磁体参数优化范围,以永磁体、气隙与槽深尺寸为优化变量,以推力体积比系数、铜耗系数为优化目标,采用基于自适应罚函数的多目标随机粒子群优化算法对作动器结构参数进行优化,并利用模糊集合理论对Pareto最优解进行选优。研究结果表明:优化后作动器结构紧凑,且作动器铜耗及波动明显降低,验证了作动器设计的正确性与多目标优化的有效性。
[Abstract]:In order to make the electromagnetic linear actuator of the active suspension system of electric wheel have the characteristics of high thrust density, low copper consumption and low thrust fluctuation, a multi-objective optimization design method for the actuator of cylindrical Halbach permanent magnet linear synchronous motor based on particle swarm optimization (PSO) is proposed. The radial air-gap magnetic field density, no-load induction electromotive force and electromagnetic force analytic formula of Halbach linear actuator are derived and verified by finite element method. Based on the parametric analysis of air gap magnetic field of Halbach magnet structure, the optimization range of magnet parameters is obtained. The optimization variables are permanent magnet, air gap and slot depth, thrust volume ratio coefficient and copper consumption coefficient. A multi-objective stochastic particle swarm optimization algorithm based on adaptive penalty function is used to optimize the structural parameters of the actuator and the fuzzy set theory is used to optimize the Pareto optimal solution. The results show that the actuator structure is compact and the copper consumption and fluctuation of the actuator are obviously reduced after optimization. The correctness of the actuator design and the effectiveness of multi-objective optimization are verified.
【作者单位】：重庆大学机械传动国家重点实验室;
【基金】：国家自然科学基金资助项目(51275541)~~
【分类号】：TP18;U463.33

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