运用路径动态预览模型的低速智能汽车侧向跟踪控制研究

王丽娟; 关龙新; 张明华; 时乐泉; 王爱春; 吴晓建

doi:10.13433/j.cnki.1003-8728.20220186

运用路径动态预览模型的低速智能汽车侧向跟踪控制研究

doi: 10.13433/j.cnki.1003-8728.20220186

王丽娟^1,,
关龙新^{1, 2},
张明华^{1, 2},
时乐泉²,
王爱春²,
吴晓建^{1, 2, ,}

1.
南昌大学机电工程学院, 南昌 330031
2.
江铃汽车股份有限公司, 南昌 330001

基金项目:

国家自然科学基金项目 52002163

国家自然科学基金项目 52062036

详细信息

作者简介:
王丽娟, 副教授, 硕士生导师, 博士, wjoker@163.com

通讯作者:
吴晓建, 副教授, 硕士生导师, 博士, saintwu520@163.com

中图分类号: U461.6
计量
- 文章访问数: 68
- HTML全文浏览量: 33
- PDF下载量: 12
- 被引次数: 0
出版历程
- 收稿日期: 2021-12-20
- 刊出日期: 2024-01-25

Research on Lateral Tracking Control of Low Speed Intelligent Vehicle Using Dynamic Path Preview Model

WANG Lijuan^1
,,
GUAN Longxin^{1, 2},
ZHANG Minghua^{1, 2},
SHI Lequan²,
WANG Aichun²,
WU Xiaojian^{1, 2
, ,}

1.
School of Mechanical & Electrical Engineering, Nanchang University, Nanchang 330031, China
2.
Jiangling Motors Co., Ltd., Nanchang 330001, China

摘要

摘要: 采用速度自适应的动态预瞄距离是提高智能车侧向跟踪控制效果的有效办法。针对目前基本采用基于当前车速的预瞄距离自适应策略，提出一种结合规划路径和规划速度信息的动态预瞄距离跟踪控制算法以提高智能车在低速侧向跟踪控制的精确性。本文以几何学纯跟踪算法为基础，推导了前行和倒车时的侧向跟踪控制律；依据实时规划的路径和速度信息，设计了预瞄距离动态调整方法，最终获取具有速度自适应性的前视距离-车辆前轮转角关系。最后，在CarSim-MATLAB/Simulink联合仿真环境下验证了算法的有效性和准确性，并通过实车测试，验证了所提出的方法较基于动力学模型的LQR算法具有更低计算消耗和更高跟踪精度。
- 智能汽车 /
- 侧向跟踪 /
- 动态预瞄距离 /
- 仿真与实车验证
Abstract: Speed adaptive dynamic preview distance is an effective way to improve the lateral tracking control effect of intelligent vehicle, and as for the problem of using current vehicle speed for the preview distance, a dynamic preview distance tracking control algorithm combining planned path and planned speed information is proposed to improve the accuracy of path tracking at low speed. Based on the pure pursue algorithm, the lateral tracking control laws of moving forward and reversing are derived. According to the real-time planned path and speed information, a dynamic adjustment method of preview distance was designed, and the relationship between the preview distance and the steering angle with speed adaptability was thereby obtained. Finally, the effectiveness and accuracy of the proposed algorithm were verified in CarSim-MATLAB/Simulink co-simulation environment, and a real vehicle test was performed, indicating that the proposed algorithm has lower computational consumption and higher tracking accuracy than the LQR algorithm based on dynamics model.
- intelligent vehicle /
- lateral tracking /
- dynamic preview distance /
- simulation verification and real vehicle validation

HTML全文

图 1 行车和倒车控制分析

Figure 1. Analysis of travel and reverse control for intelligent vehicle

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图 2 几何学跟踪控制模型与参考路径

Figure 2. Geometric tracking control model and its reference path

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图 3 基于路径动态预览模型的侧向跟踪算法

Figure 3. Lateral tracking algorithm based on the dynamic preview path model

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图 4 双移线与复杂场景工况

Figure 4. Double-lane change and complex scenario conditions

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图 5 测试场景一行车时的路径跟踪控制效果(7 km/h)

Figure 5. Path tracking control performance in test scenario one while traveling (7 km/h)

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图 6 测试场景一倒车时的路径跟踪控制效果(7 km/h)

Figure 6. Path tracking control performance in test scenario one while reversing (7 km/h)

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图 7 测试场景二-中低速工况跟踪控制效果(28 km/h)

Figure 7. Path tracking control performance for low-speed conditions in test scenario two (28 km/h)

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图 8 低速自主泊车系统架构

Figure 8. Low-speed autonomous parking system architecture

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图 9 车载天线和IMU

Figure 9. Vehicle antenna and IMU

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图 10 本文算法与线性LQR算法侧向跟踪误差效果比对

Figure 10. Comparison of the effect of lateral tracking error between proposed algorithm and linear LQR algorithm

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图 11 本文算法与线性LQR算法航向跟踪误差效果比对

Figure 11. Comparison of the effect of yaw tracking error between proposed algorithm and linear LQR algorithm

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图 12 本文算法与线性LQR算法计算时间对比

Figure 12. Comparison of computation time between proposed algorithm and linear LQR algorithm

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图 13 本文算法在自主泊车场景的应用

Figure 13. Application of proposed algorithm to autonomous parking scenarios

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表 1 实车测试车辆参数

Table 1. Tested vehicle parameters

参数	数值
智能汽车质量/kg	1 830
质心到前轴的距离/m	1.276
质心到后轴的距离/m	1.589
转动惯量/(kg·m²)	3 710.41
前轮侧偏刚度/(N·rad^-1)	-150 000
后轮侧偏刚度/(N·rad^-1)	-150 000
转向系角传动比i_sw	15.9

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参考文献(16)

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