基于粒子群算法的麦弗逊悬架硬点优化  

Optimization for Macpherson suspension hard points based on PSO algorithms

作　　者：张军 石琴 陈一锴 ZHANG Jun;SHI Qin;CHEN Yikai(School of Automobile and Traffic Engineering,Hefei University of Technology,Hefei 230009,China)

机构地区：[1]合肥工业大学汽车与交通工程学院,安徽合肥230009

出　　处：《合肥工业大学学报（自然科学版）》2018年第11期1466-1472,共7页Journal of Hefei University of Technology：Natural Science

基　　金：国家自然科学基金资助项目(51305117);中国博士后科学基金资助项目(2013M530230;2014T70464);高等学校博士学科点专项科研基金资助项目(20130111120031);安徽省科技攻关资助项目(1501b042211)

摘　　要：文章依据空间解析几何法构建麦弗逊悬架的数学模型,通过与ADAMS模型的对比,验证了模型的精度;采用Screening法和正交试验筛选出对悬架特性有显著影响的硬点坐标,拟合得到各定位参数的响应面函数;依据目标规划法确定各目标函数的加权因子,确定多目标优化模型;基于粒子群优化(particle swarm optimization,PSO)算法实现悬架硬点坐标的多目标优化。结果表明,轮跳试验中,优化后的车轮定位参数变化量比优化前减小了18.10%~30.32%,验证了该文提出的麦弗逊悬架硬点优化方法的有效性。A mathematical model of Macpherson suspension was developed based on the spatial analytic geometry method, whose accuracy was validated against an ADAMS model. Then the hard point coor dinates which had significant impact on suspension performance were chosen with Screening method and orthogonal test, and the response surface functions of front wheel alignment parameters were fit ted based on simulation results. Finally, the weight factors of obiective functions were determined by goal programming method, and the multi objective optimization model was gotten. And the hard point coordinates were optimized using particle swarm optimization（PSO） algorithms. The results show that the changes of the alignment parameters are reduced by 18. 10% to 30.32% using the optimized hard points, compared with those with the original hard points. Thus, the proposed method is proved to be effective.

关 键 词：麦弗逊悬架 正交试验 响应面函数 粒子群优化(PSO)算法 

分 类 号：U270.1[机械工程—车辆工程]