摘要
通过主动悬架的精确控制提高车辆乘坐舒适性与行驶安全性的基本前提是进行路面高程与等级识别。本文中设计了考虑未知输入的卡尔曼观测器,以获取路面高程信息;根据路面高程建立AR模型,得到路面功率谱密度,并求取兴趣频段内路面功率谱密度均方根值,实现了路面的等级分类。仿真分析了不同工况下路面高程估计方法和路面等级分类方法的准确性,并搭建了试验台架,验证了所提出路面高程估计方法和路面等级分类方法的有效性,为主动悬架的智能控制提供了必要条件。
The basic premise of improving the ride comfort and driving safety of vehicles via the precise control of active suspension is road elevation and grade identification.In this paper,a Kalman observer considering unknown inputs is designed to obtain the road elevation information.The AR model is established to acquire the road power spectral density,and the root mean square value of the road power spectral density in the interest fre⁃quency band is computed to realize the road grade classification.The accuracy of road elevation estimation and road grade classification under different working conditions is analyzed by simulation.Finally,the test bench is built to verify the effectiveness of the proposed road elevation estimation and road grade classification method,which pro⁃vides necessary conditions for the intelligent control of active suspension.
作者
丁仁凯
蒋俞
汪若尘
刘伟
孟祥鹏
孙泽宇
Ding Renkai;Jiang Yu;Wang Ruochen;Liu Wei;Meng Xiangpeng;Sun Zeyu(Automotive Engineering Research Institute,Jiangsu University,Zhenjiang 212013;School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang 212013)
出处
《汽车工程》
EI
CSCD
北大核心
2021年第2期278-286,共9页
Automotive Engineering
基金
国家自然科学基金(51975253)资助。
关键词
主动悬架
未知输入
卡尔曼观测器
路面高程估计
路面等级分类
active suspension
unknown inputs
Kalman observer
road elevation estimation
road grade classification
