帕累托耦合遗传算法对车辆振动模型优化与动力学仿真  

Optimization and dynamics simulation of vehicle vibration model based on Pareto coupled genetic algorithm

作　　者：黄小兵[1] 

机构地区：[1]攀枝花学院机械工程学院,四川攀枝花617000

出　　处：《中国工程机械学报》2017年第2期113-118,共6页Chinese Journal of Construction Machinery

基　　金：国家自然科学基金重点资助项目(51134004)

摘　　要：为研究车辆行驶产生振动严重问题,提高车辆座椅的舒适性,构造了5自由度车辆振动模型,推导出车辆行驶运动控制微分方程,采用帕累托耦合遗传算法对5自由度车辆振动模型的五目标函数进行优化.优化对象包括前轮速度、后轮速度、簧载质量和前轮的相对位移、簧载质量和后轮的相对位移及车辆座椅垂直方向加速度.在五目标函数优化处理后,找出最佳运动学仿真优化值,通过Matlab/Simulation软件进行动力学仿真,与其他优化方法进行对比.仿真结果显示,在同等条件下,帕累托耦合遗传算法优化后的车辆经过地面凸起障碍物时,车辆座椅垂直方向产生加速度峰值降低了50%,车辆抖动次数较少.帕累托耦合遗传算法对车辆振动模型优化后,车辆行驶过障碍物相对平稳,改善了车辆座椅在行驶过程中的舒适性.In order to study the serious vibration problem of vehicle driving,improve the comfort of the vehicle seat.A vehicle vibration model with five degrees of freedom is constructed,and the differential equations of motion control are derived.The five-objective function of the model of five degrees of freedom vehicle vibration model is optimized by using Pareto coupled genetic algorithm.The optimization object includes front wheel speed,rear wheel speed,relative displacement,relative displacement,relative displacement and vertical acceleration of vehicle seat.After optimization of the five-objective function,to find the best kinematics simulation optimization value,through the Matlab/Simulation software for dynamic simulation,and other optimization methods are compared.Simulation results show that the Pareto coupled genetic algorithm optimization of the vehicle through the raised floor of obstacles,in the vertical direction of the vehicle seat produce peak acceleration is reduced by50% under the same conditions,vehicle shaking times less.Pareto coupled genetic algorithm for vehicle vibration model optimization,the relative stability of vehicle moving over obstacles,improve the comfort of the vehicle seat in the process of moving.

关 键 词：帕累托 遗传算法 车辆振动模型 五目标 动力学仿真 优化 

分 类 号：U463[机械工程—车辆工程]