基于渐近神经网络的汽车前轮定位参数反求  

Identification of Front Wheel Alignment Based on the Progressive Neutral Network Method

作　　者：莫旭辉[1] 韩旭[1] 钟志华[1] 

机构地区：[1]湖南大学汽车车身先进设计制造国家重点实验室,机械与汽车工程学院,湖南长沙410082

出　　处：《湖南大学学报（自然科学版）》2008年第6期18-22,共5页Journal of Hunan University:Natural Sciences

基　　金：新世纪优秀人才计划(NECT-04-0766);973项目(2004CB719402)

摘　　要：利用ADAMS/CAR软件建立了包含轮胎、橡胶衬套的双横臂扭杆独立悬架和转向系统的动力学模型,分析了前轮定位参数对汽车转向力矩的影响,利用渐进的神经网络方法建立前轮定位参数和转向力矩的关系.结果表明:采用渐进神经网络方法反求得到前轮定位参数能够很好满足预先设定的转向力矩要求.A double wishbone suspension with a torsion bar and a steering system including the tire and the rubber insert was built using ADAMS/CAR, and the effect of the front wheel alignment on the steering wheel torque was investigated. A progressive neural network was adopted to investigate the relationship between the front wheel alignment and the steering wheel torque. The results have proved that the front wheel alignment identified by this progressive neural network method can suit the steering wheel torque.

关 键 词：前轮定位 转向 参数反求 汽车 神经网络 

分 类 号：U461[机械工程—车辆工程]