基于LSTM网络的路面不平度辨识方法  

Road Unevenness Identification Based on LSTM Network

作　　者：梁冠群 赵通 王岩[1] 危银涛[1] Liang Guanqun;Zhao Tong;Wang Yan;Wei Yintao(School of Vehicle and Mobility,Tsinghua University,State Key Laboratory of Automotive Safety and Energy,Beijing 100084)

机构地区：[1]清华大学车辆与运载学院,汽车安全与节能国家重点实验室,北京100084

出　　处：《汽车工程》2021年第4期509-517,628,共10页Automotive Engineering

基　　金：国家自然科学基金(51761135124,11672148);汽车安全与节能国家重点实验室基金(20194180043)资助。

摘　　要：路面不平度的辨识是半主动悬架控制等智能底盘技术的难点,目前缺乏成本低、可靠性高且精确快速的方法。本文提出一种新的基于长短期记忆(long short-term memory,LSTM)网络和时序轮心加速度的实时路面不平度等级辨识方法。该方法利用轮心加速度的时序信号,而非传统的统计特征,基于LSTM网络对时序信号的强特征捕捉能力,无需信号预处理就能快速得到路面分类特征,大大减少辨识算法的计算量,可以实现实时辨识。训练集数据可以通过不同功率谱密度等级的白噪声生成路面,然后通过车辆传递特性获得加速度信号,也可集成试验数据。此方法仅需要一个时域加速度信号,且无需复杂预处理,鲁棒性高,能够实现不同车速、减振器阻尼系数、簧上质量和采样时长下的路面不平度等级的快速辨识。The identification of road roughness is one key technology of smart chassis such as semi-active suspension control.There is a lack of cheap,reliable,accurate and rapid method currently.This paper proposes a new real-time road roughness level identification method based on LSTM(Long Short-Term Memory)network and sequential wheel center acceleration.This method adopts sequential signals of wheel center acceleration instead of traditional statistical features.Based on the LSTM network’s strong feature capture capability for sequential signals,it can rapidly obtain road classification features without signal preprocessing,greatly reducing the calculation burden to realize real-time identification.For training set data,the acceleration signal can be obtained from experimental data,or calculated through vehicle transfer characteristics with white-noise-generated road with different power spectral density levels.This method requires only one time-domain acceleration signal without complex preprocessing.It can achieve rapid identification of road roughness grades at different vehicle speeds,damping coefficients,sprung mass and sampling time with high robustness.

关 键 词：路面辨识 LSTM 信号处理 深度学习 悬架控制 

分 类 号：U463[机械工程—车辆工程]