基于集合卡尔曼滤波的高机动救援车辆主动悬挂控制方法  

Active suspension control method of high mobility rescue vehicle based on ensembleKalmanfilter

作　　者：李文航 倪涛 赵丁选 张泮虹 师小波 Wen-hang LI;Tao NI;Ding-xuan ZHAO;Pan-hong ZHANG;Xiao-bo SHI(College of Mechanical and Aerospace Engineering,Jilin University,Changchun 130022,China;School of Vehicle and Energy,Yanshan University,Qinhuangdao 066004,China)

机构地区：[1]吉林大学机械与航空航天工程学院,长春130022 [2]燕山大学车辆与能源学院,河北秦皇岛066004

出　　处：《吉林大学学报（工学版）》2022年第12期2816-2826,共11页Journal of Jilin University:Engineering and Technology Edition

基　　金：河北省创新群体项目(E2020203174);国家自然科学基金重点项目(U20A20332).

摘　　要：针对高机动救援车辆提出一种主动悬挂系统控制策略——基于集合卡尔曼滤波技术的模型预测控制策略(EnKF-MPC)。首先,对高机动救援车辆主动悬挂系统进行动力学建模,通过集合卡尔曼滤波技术完成车辆动力学系统和车载组合导航系统的数据融合,实现车辆位姿信息的精确估计;针对组合导航系统垂向位移误差较大的问题,设计了点云匹配算法,完成车辆垂向位移信息的精确评估。其次,提出了模型预测控制策略,将集合卡尔曼滤波算法得到的车辆位姿信息和车载雷达系统获取的道路信息作为系统输入对车辆主动悬挂系统进行实时控制。最后,进行了实车验证,结果表明,提出的车辆位姿估计算法垂向位移误差为±3.100 cm,俯仰角误差为±0.175°,侧倾角误差为±0.210°。相比于被动悬挂,提出的主动悬挂控制方法垂向位移均方根平均值降低37%,俯仰角度均方根平均值降低35%,侧倾角度均方根平均值降低35%,显著提升了车辆的行驶平顺性和操纵稳定性。A control strategy of active suspension systems was proposed for high-mobility rescue vehicles—model predictive control strategy based on ensemble Kalman Filter technology(EnKF-MPC).Firstly,dynamic model of the active suspension system was completed for high-mobility rescue vehicles.The data of the vehicle dynamic system and the vehicle-mounted positioning system were fused through the Ensemble Kalman Filter technology in order to realize the accurate estimation of the vehicle's pose information;Aiming at the problem of vertical positioning error for the vehicle positioning system,a point cloud matching algorithm was designed to complete the accurate evaluation of the vehicle's vertical direction information;in addition,a model predictive control strategy was proposed,which used the vehicle's pose information obtained by the ensemble Kalman filter algorithm and the road profile information obtained by the on-board lidar as system inputs to control the active suspension system of the vehicle to improve the ride comfort and handling stability of the vehicle.Finally,a real vehicle test was carried out.The research results show that the vertical direction error of the proposed vehicle pose estimation algorithm is about±3.100?cm,the pitch angle error is about±0.175°,and the roll angle error is about±0.210°.Compared with the passive suspension system,the proposed active suspension control method reduced the root mean square value of the vertical displacement by 37%,the pitch angle by 35%,and the roll angle by 35%,which significantly improved the ride comfort and handling stability of the vehicle.

关 键 词：公路运输 主动悬挂系统 集合卡尔曼滤波 模型预测控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]