无人车辆横向跟踪控制研究

李伯雄; 王立勇; 孙鹏; 季文龙

doi:10.13433/j.cnki.1003-8728.20220204

无人车辆横向跟踪控制研究

doi: 10.13433/j.cnki.1003-8728.20220204

1.
北京信息科技大学现代测控技术教育部重点实验室，北京　100192
2.
中国人民解放军 63966部队，北京　100072

基金项目: 北京信息科技大学 “勤信人才” 培育计划项目(QXTCPA201903，QXTCPB201901)与促进内涵发展科研水平提高项目（2020KYNH112）

详细信息

作者简介:
李伯雄，硕士研究生，810031041@qq.com

通讯作者:
王立勇，教授，博士生导师，wangliyong@bistu.edu.cn

中图分类号: U46
计量
- 文章访问数: 95
- HTML全文浏览量: 19
- PDF下载量: 34
- 被引次数: 0
出版历程
- 收稿日期: 2021-11-19
- 网络出版日期: 2024-03-08
- 刊出日期: 2024-02-01

Research on Lateral Tracking Control of Autonomous Vehicles

1.
The Ministry of Education Key Laboratory of Modern Measurement and Control Technology, Beijing Information Science and Technology University, Beijing 100192, China
2.
Unit 63966 of the Chinese People's Liberation Army, Beijing 100072, China

摘要

摘要: 使用传统模型预测控制对车辆轨迹进行跟踪时，模型中的路面附着系数往往为特定工况下的经验数值。当车辆在未知路面行驶时，现有控制算法难以对路面附着系数进行及时修正，并调整预测控制模型内的约束，进而导致车辆横向失稳。针对此种情况，提出一种考虑实时路面附着系数估计的横向跟踪控制策略，用于实现车辆横向轨迹跟踪。该算法针对路面附着系数未知的工况，利用车辆当前横纵向加速度、横摆角速度、前轮转角等状态量，通过扩展卡尔曼滤波预测路面附着系数后，再对控制模型中的侧偏角约束量进行实时调整，以保证车辆在未知路面工况下的行驶安全，使车辆跟随预期轨迹行驶。实验表明，将扩展卡尔曼滤波法与模型预测控制结合的控制算法具有可行性，且有效提高了车辆在不同附着系数路面行驶时横向轨迹跟踪的稳定性及鲁棒性。
- 模型预测控制 /
- 扩展卡尔曼滤波 /
- 路面附着系数预测 /
- 轨迹跟踪控制
Abstract: The traditional model predictive control method is used to track the vehicle trajectory, the road adhesion coefficient in the model is often fixed as the empirical value of the current road. When the vehicle is driving on unknown road, it is difficult to adjust the inboard constraint of predictive control model in real time, and it is difficult to correct the road adhesion coefficient in time, which leads to vehicle lateral instability. In this paper, a model predictive control algorithmconsidering real-time road adhesion coefficient is proposed to realize trajectory tracking. Under the condition of unknown road friction coefficient, the algorithm predicts the road adhesion coefficient through the vehicle's current transverse and longitudinal acceleration, yaw rate, front wheel angle and other state variables, and adjusts the yaw angle constraint in the control model in real time. Even under the unknown road surface, it can provide redundant protection for vehicle control and ensure the vehicle to follow the expected trajectory.
- model predictive control /
- extended Kalman filter /
- prediction of pavement adhesion coefficient /
- trajectory tracking control

HTML全文

图 1 四自由度车辆动力学模型

Figure 1. Four degree of freedom vehicle's dynamics model

下载: 全尺寸图片幻灯片

图 2 扩展卡尔曼预测结果

Figure 2. Extended Kalman prediction results of road adhesion coefficient

下载: 全尺寸图片幻灯片

图 3 横向跟踪控制算法结构

Figure 3. Structure of lateral tracking control algorithm

下载: 全尺寸图片幻灯片

图 4 附着系数μ = 0.20的控制效果

Figure 4. Control effect when the adhesion coefficient μ = 0.2

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图 5 附着系数μ = 0.50的控制效果

Figure 5. Control effect when the adhesion coefficient μ = 0.50

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图 6 附着系数μ = 0.85的控制效果

Figure 6. Control effect when the adhesion coefficient μ=0.85

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表 1 方程式车主要参数

Table 1. Main parameters of formula vehicle

参数名称	数值
整车质量m/kg	250
质心到前轴距离a/m	0.995
质心到后轴距离b/m	0.665
前轮距t_f /m	1.27
后轮距t_r /m	1.28
轮胎滚动半径R_W/m	0.228
横摆转动惯量I_z/（kg·m²）	1410
轮胎侧偏刚度C_y/（N·rad⁻¹）	32000

下载: 导出CSV

表 2 侧偏角与路面附着系数对应关系

Table 2. Lateral deviation angle and road adhesion coefficient correspondence

附着系数μ	侧偏角约束β/（°）
0.2	2.2
0.5	5.5
0.85	9.4

下载: 导出CSV

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