融合稳定性的高速无人驾驶车辆纵横向协调控制方法

李军; 唐爽; 黄志祥; 周伟

doi:10.19818/j.cnki.1671-1637.2020.02.017

融合稳定性的高速无人驾驶车辆纵横向协调控制方法

doi: 10.19818/j.cnki.1671-1637.2020.02.017

李军^{1, 2,},
唐爽^1,,
黄志祥¹,
周伟¹

1.
重庆交通大学机电与车辆工程学院, 重庆 400074
2.
重庆交通大学城市轨道交通车辆系统集成与控制重庆市重点试验室, 重庆 400074

基金项目:

国家自然科学基金项目 51705051

重庆市自然科学基金项目 cstc2018jcyjAX0422

详细信息

作者简介:
李军(1964-), 男, 重庆人, 重庆交通大学教授, 工学博士, 从事新能源汽车和智能车辆与控制研究

通讯作者:
唐爽(1995-), 男, 重庆人, 重庆交通大学工程硕士研究生

中图分类号: U491.2
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- 被引次数: 0
出版历程
- 收稿日期: 2019-11-14
- 刊出日期: 2020-04-25

Longitudinal and lateral coordination control method of high-speed unmanned vehicles with integrated stability

1.
College of Mechanical and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China
2.
Chongqing Key Laboratory of Rail Vehicle System Integration and Control, Chongqing Jiaotong University, Chongqing 400074, China

Funds:

National Natural Science Foundation of China 51705051

Natural Science Foundation of Chongqing cstc2018jcyjAX0422

More Information

Author Bio:
LI Jun(1964-), male, professor, PhD, E-mail: cqleejun@163.com

Corresponding author: LI Jun(1964-), male, professor, PhD, E-mail: cqleejun@163.com

Article Text (Baidu Translation)

摘要

摘要: 提出了一种纵横向协调控制的路径跟踪控制方法; 建立了车辆预瞄误差模型和考虑路面地形的高速车辆等效动力学模型, 以此引入道路曲率地形因素; 基于模糊规则设计了预瞄距离发生器, 解决预瞄误差模型中固定预瞄距离的问题; 建立了预测时域与道路曲率的函数关系, 运用模型预测控制算法求解前轮转角, 从而建立路径跟踪控制器; 运用指数模型表示车辆期望车速, 设计了比例积分微分纵向控制器控制车速以改善路径跟踪精度; 运用质心侧偏角相平面图表征车辆稳定性特征, 设计比例积分微分稳定性控制器以改善车辆稳定性。研究结果表明: 提出的控制方法能在不同附着系数路面上对车辆跟踪性能进行优化, 在干燥沥青路面以车速90 km·h^-1行驶时, 与只运用模型预测控制算法进行路径跟踪控制的车辆相比, 最大横向误差可减少33%;在潮湿沥青路面以车速70 km·h^-1行驶时, 与只运用模型预测控制算法进行路径跟踪控制的车辆相比, 最大横向误差可减少30%;在冰雪路面以车速55 km·h^-1行驶时, 与只运用模型预测控制算法进行路径跟踪控制的车辆相比, 最大横向误差可减少16%。可见, 所提出的控制方法能有效改善路径跟踪精度。
- 无人驾驶车辆 /
- 路径跟踪 /
- 纵横向控制 /
- 模糊控制 /
- 稳定性
Abstract: A path tracking control method considering longitudinal and lateral coordination control was proposed. The vehicle preview error model and high-speed vehicle equivalent dynamics model considering road surface terrain were established to introduce road curvature terrain factors. The preview distance generator based on the fuzzy rules was designed to solve the problem of fixed preview distance in the preview error model. The function relationship between the time domain and the road curvature was established. The model predictive control algorithm was used to solve the front wheel rotation angle, thereby establishing a path tracking controller. The expected vehicle speed was represented by the exponential model, and the proportion integration differentiation longitudinal controller was designed to improve the path tracking accuracy. The vehicle stability characteristic was represented by phase plane of slip angle, and the proportion integration differentiation stability controller was designed to improve the vehicle stability. Research result shows that the control method can optimize the vehicle tracking performance on the roads with different adhesion coefficients. When driving on a dry asphalt pavement at a speed of 90 km·h^-1, the maximum lateral error reduces by 33% compared with a vehicle that only uses model predictive control algorithm for path tracking control. When driving on a wet asphalt pavement at a speed of 70 km·h^-1, the maximum lateral error reduces by 30% compared with a vehicle that only uses model predictive control algorithm for path tracking control. When driving on an icy and snow pavement at a speed of 55 km·h^-1, the maximum lateral error reduces by 16% compared with a vehicle that only uses model predictive control algorithm for path tracking control. Therefore, the proposed control method can effectively improve the path tracking accuracy.
- unmanned vehicle /
- path tracking /
- longitudinal and lateral control /
- fuzzy control /
- stability

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图 1 车辆动力学模型

Figure 1. Vehicle dynamics model

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图 2 车轮侧向力与侧偏角关系曲线

Figure 2. Relationship curves between lateral force and side angle of tire

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图 3 车辆预瞄跟踪模型

Figure 3. Preview tracking model of vehicle

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图 4 预瞄距离

Figure 4. Preview distances

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图 5 质心侧偏角相平面

Figure 5. Phase plane of side slip angle

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图 6 稳定性控制器控制流程

Figure 6. Control flow of stability controller

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图 7 双移线

Figure 7. Double shift line

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图 8 70km·h^-1干燥沥青路面路径跟踪仿真结果

Figure 8. Path tracking simulation results on dry asphalt pavement at 70 km·h^-1

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图 9 90km·h^-1干燥沥青路面路径跟踪仿真结果

Figure 9. Path tracking simulation results on dry asphalt pavement at 90 km·h^-1

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图 10 60km·h^-1潮湿沥青路面路径跟踪仿真结果

Figure 10. Path tracking simulation results on wet asphalt pavement at 60 km·h^-1

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图 11 70km·h^-1潮湿沥青路面路径跟踪仿真结果

Figure 11. Path tracking simulation results on wet asphalt pavement at 70 km·h^-1

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图 12 50km·h^-1冰雪路面路径跟踪仿真结果

Figure 12. Path tracking simulation results on icy and snowy pavement at 50 km·h^-1

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图 13 55km·h^-1冰雪路面路径跟踪仿真结果

Figure 13. Path tracking simulation results on icy and snowy pavement at 55 km·h^-1

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表 1 制动规则

Table 1. Braking rules

工况		转向特性	制动情况
1	δ > 0, e₄ > 0	过多转向	右侧轮
2	δ > 0, e₄ < 0	不足转向	左侧轮
3	δ < 0, e₄ < 0	过多转向	左侧轮
4	δ < 0, e₄ > 0	不足转向	右侧轮

下载: 导出CSV

表 2 车辆基本参数

Table 2. Vehicle basic parameters

参数	数值
m/kg	1 723
I₁/(kg·m²)	4 175
I₂/(kg·m²)	1 243
H₁/(N·m·rad^-1)	145 330
H₂/(N·m·rad^-1)	4 500
a/m	1.232
b/m	1.468
A₁/(N·rad^-1)	66 900
A₂/(N·rad^-1)	62 700
W/m	0.3
h/m	0.54

下载: 导出CSV

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