基于免疫算法的悬架系统参数的辨识

在ADAMS中建立了麦弗逊与双横臂两种不同悬架形式的1/4汽车模型,依据其输入输出数据,利用免疫算法分别对两者的车身质量、悬架刚度与阻尼、轮胎的刚度及轮胎质量进行了辨识.仿真结果表明:使用辨识参数的简化模型可以替代真实模型并能够合理反映出由于悬架结构不同而引起的性能差异;免疫算法是参数辨识的一种有效方法,具有较好的全局寻优特性.

Parameters Identification for Automotive Suspension Based on Immune Algorithm

On the basis of the input and output of mcpherson-type and double-wishbone-type quarter-car models built in ADAMS,the immune algorithm was applied to identify the sprung mass,the suspension stiffness and damp,the tyre vertical stiffness and the unsprung mass of these two different models.Simulation results showed that the simple identification models were equivalent to the real suspension models and could reflect the difference of suspension behavior due to the difference of structure.Moreover,the immune algorithm,which had good global optimization performance,was proved to be an effective method in parameter identification.