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

doi:10.13229/j.cnki.jdxbgxb20211448

摘要/Abstract

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。

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

Abstract:

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 $± 3.100 ? c m$ ， 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.

Key words: road transportation, active suspension system, ensemble Kalman filter, model predictive control

中图分类号:

TP273

李文航,倪涛,赵丁选,张泮虹,师小波. 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[J]. 吉林大学学报(工学版), 2022, 52(12): 2816-2826.

Wen-hang LI,Tao NI,Ding-xuan ZHAO,Pan-hong ZHANG,Xiao-bo SHI. Active suspension control method of high mobility rescue vehicle based on ensemble Kalman filter[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 2816-2826.

图/表 10

图1

图2

图3

表1

车辆参数"

符号	含义	数值	单位
$M$	车身质量	3.6×10⁴	kg
$T$	轮胎的宽度	0.4	m
$H$	车体中心高度	2.1	m
$V$	平均行驶速度	5.00	km/h
$J Y$	Y轴转动惯量	2.46×10⁵	kg·m²
$J X$	X轴转动惯量	1.12×10⁵	kg·m²
$K m i$	轮胎刚度系数	1.90×10⁶	N/m

表1

图4

图5

表2

图6

图7

表3

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路面类型	参数	未融合	EnKF
图4（b）	垂向误差/cm	4.300	2.950
	俯仰角误差/（°）	0.195	0.155
	侧倾角误差/（°）	0.216	0.190
图4（c）	垂向误差/cm	4.630	3.100
	俯仰角误差/（°）	0.216	0.175
	侧倾角误差/（°）	0.253	0.210

路面类型	参数	PAS	MPC	EnKF-MPC
图4（c）	垂向/m	0.0535	0.0417	0.0338
	俯仰角/（°）	0.5500	0.4235	0.3575
	侧倾角/（°）	0.2650	0.1988	0.1722

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