路面功率谱密度识别的仿真  

Simulation of Road Surface Power Spectrum Density Identification

作　　者：张丽霞[1] 赵又群[1] 徐培民[2] 吴杰[1] 

机构地区：[1]南京航空航天大学能源与动力学院 [2]安徽工业大学机械工程学院

出　　处：《农业机械学报》2007年第5期15-18,共4页Transactions of the Chinese Society for Agricultural Machinery

基　　金：高等学校博士学科点专项科研基金项目(项目编号:20040287004);江苏省博士后科研基金项目(项目编号:2004300)

摘　　要：路面激励是汽车平顺性和操纵稳定性研究中的重要因素。提出了一种基于径向基函数(RBF)神经网络识别路面功率谱密度的仿真研究方法。建立了4自由度汽车振动模型,利用Matlab软件仿真得到汽车车身质心垂直加速度功率谱密度和俯仰角加速度功率谱密度。应用RBF神经网络建立了汽车车身质心垂直加速度功率谱密度、俯仰角加速度功率谱密度和路面功率谱密度之间的非线性映射模型。仿真结果表明:该方法思路明确,抗噪声能力比较强,识别的精确度高。Road surface excitation is an important factor in the research of vehicle riding and maneuver stability. Based on radial basis function （RBF） neural networks, a simulation research method of road surface power spectrum density （PSD） identification was put forward. A four-degree-of- freedom vehicle vibration model was set up and the PSDs for vertical acceleration and pitching angular acceleration of vehicle body centroid were got through Matlab simulation. The nonlinear mapping relation among the PSD of vehicle body centroid vertical acceleration, pitching angular acceleration and the road surface were found by RBF neural networks. The simulation results show that the proposed method has the feature of a good anti-noise performance and high identification accuracy.

关 键 词：载荷识别 路面功率谱密度 径向基函数神经网络 仿真 

分 类 号：U461.4[机械工程—车辆工程]