基于滚动时域强化学习的智能车辆侧向控制算法

张兴龙; 陆阳; 李文璋; 徐昕

doi:10.16383/j.aas.c210555

基于滚动时域强化学习的智能车辆侧向控制算法

doi: 10.16383/j.aas.c210555

张兴龙^1,,
陆阳^1,,
李文璋^1,,
徐昕^1,

1.
国防科技大学智能科学学院长沙 410073

基金项目: 国家重点研究发展计划 (2018YFB1305105), 国家自然科学基金(62003361, 61825305) 资助

详细信息

作者简介:
张兴龙：国防科技大学智能科学学院副研究员. 2018年获得意大利米兰理工大学博士学位. 主要研究方向为滚动时域强化学习及其在无人系统中的应用. E-mail: zhangxinglong18@nudt.edu.cn

陆阳：国防科技大学智能科学学院博士研究生. 2020年获得国防科技大学硕士学位, 2018年获得山东大学学士学位. 主要研究方向为强化学习及其在无人系统中的应用. E-mail: luyang18@nudt.edu.cn

李文璋：2018年获得北京理工大学学士学位, 2020年获得国防科技大学硕士学位. 主要研究方向为智能车学习控制. E-mail: 15624953231@163.com

徐昕：国防科技大学智能科学学院研究员. 2002年获得国防科技大学机电与自动化学院控制科学与工程博士学位. 主要研究方向为智能控制, 强化学习, 近似动态规划, 机器学习, 机器人和智能驾驶. 本文通信作者. E-mail: xinxu@nudt.edu.cn

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出版历程
- 收稿日期: 2021-06-20
- 录用日期: 2021-11-02
- 网络出版日期: 2022-03-07
- 刊出日期: 2023-12-27

Receding Horizon Reinforcement Learning Algorithm for Lateral Control of Intelligent Vehicles

1.
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073

Funds: Supported by National Key Research and Development Program of China (2018YFB1305105) and National Natural Science Foundation of China (62003361, 61825305)

More Information

Author Bio:
ZHANG Xing-Long　Associate professor at the College of Intelligence Science and Technology, National University of Defense Technology. He received his Ph.D. degree from Politecnico di Milano, Italy, in 2018. His research interest covers receding horizon reinforcement learning and its application in unmanned systems

LU Yang　Ph.D. candidate at the College of Intelligence Science and Technology, National University of Defense Technology. He received his master and bachelor degrees from National University of Defense Technology in 2020 and Shandong University in 2018, respectively. His research interest covers reinforcement learning and its application in unmanned systems

LI Wen-Zhang　Received his bachelor and master degrees from National University of Defense Technology in 2018 and Beijing Institute of Technology in 2020, respectively. His research interest covers learning control of intelligent vehicles

XU Xin　Professor at the College of Intelligence Science and Technology, National University of Defense Technology. He received his Ph.D. degree in control science and engineering from the College of Mechatronics and Automation, National University of Defense Technology in 2002. His research interest covers intelligent control, reinforcement learning, approximate dynamic programming, machine learning, robotics, and autonomous vehicles. Corresponding author of this paper

摘要

摘要: 针对智能车辆的高精度侧向控制问题, 提出一种基于滚动时域强化学习(Receding horizon reinforcement learning, RHRL)的侧向控制方法. 车辆的侧向控制量由前馈和反馈两部分构成, 前馈控制量由参考路径的曲率以及动力学模型直接计算得出; 而反馈控制量通过采用滚动时域强化学习算法求解最优跟踪控制问题得到. 提出的方法结合滚动时域优化机制, 将无限时域最优控制问题转化为若干有限时域控制问题进行求解. 与已有的有限时域执行器−评价器学习不同, 在每个预测时域采用时间独立型执行器−评价器网络结构学习最优值函数和控制策略. 与模型预测控制(Model predictive control, MPC)方法求解开环控制序列不同, RHRL控制器的输出是一个显式状态反馈控制律, 兼具直接离线部署和在线学习部署的能力. 此外, 从理论上证明了RHRL算法在每个预测时域的收敛性, 并分析了闭环系统的稳定性. 在仿真环境中完成了结构化道路下的车辆侧向控制测试. 仿真结果表明, 提出的RHRL方法在控制性能方面优于现有先进算法, 最后, 以红旗E-HS3电动汽车作为实车平台, 在封闭结构化城市测试道路和乡村起伏砂石道路下进行了侧向控制实验. 实验结果显示, RHRL在结构化城市道路中的侧向控制性能优于预瞄控制, 在乡村道路中具有较强的路面适应能力和较好的控制性能.
- 滚动时域 /
- 强化学习 /
- 智能汽车 /
- 侧向控制
Abstract: This paper presents a receding horizon reinforcement learning (RHRL) algorithm for realizing high-accuracy lateral control of intelligent vehicles. The overall lateral control is composed of a feedforward control term that is directly computed using the curvature of the reference path and the dynamic model, and a feedback control term that is generated by solving an optimal control problem using the proposed RHRL algorithm. The proposed RHRL adopts a receding horizon optimization mechanism, and decomposes the infinite-horizon optimal control problem into several finite-horizon ones to be solved. Different from existing finite-horizon actor-critic learning algorithms, in each prediction horizon of RHRL, a time-independent actor-critic structure is utilized to learn the optimal value function and control policy. Also, compared with model predictive control (MPC), the control learned by RHRL is an explicit state-feedback control policy, which can be deployed directly offline or learned and deployed synchronously online. Moreover, the convergence of the proposed RHRL algorithm in each prediction horizon is proven and the stability analysis of the closed-loop system is peroformed. Simulation studies on a structural road show that, the proposed RHRL algorithm performs better than current state-of-the-art methods. The experimental studies on an intelligent driving platform built with a Hongqi E-HS3 electric car show that RHRL performs better than the pure pursuit method in the adopted structural city road scenario, and exhibits strong adaptability to road conditions and satisfactory control performance in the country road scenario.
- Receding horizon /
- reinforcement learning /
- intelligent vehicles /
- lateral control

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图 1 智能车辆二自由度侧向模型

Fig. 1 Two-degree-of-freedom lateral model of intelligent vehicle

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图 2 侧向误差模型

Fig. 2 Lateral error model

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图 3 智能车侧向控制框图

Fig. 3 Lateral control diagram of intelligent vehicle

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图 4 参考路径

Fig. 4 Reference path

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图 5 30 ${\rm{km/h}}$下智能车跟踪控制侧向偏差对比

Fig. 5 Comparison of lateral tracking error of intelligent vehicles under $v_x = 30 \; {\rm{km/h}}$

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图 6 50 ${\rm{km/h}}$下智能车跟踪控制侧向偏差对比

Fig. 6 Comparison of lateral tracking error of intelligent vehicles under $v_x = 50 \; {\rm{km/h }}$

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图 7 红旗E-HS3智能驾驶平台

Fig. 7 Hongqi E-HS3 intelligent driving platform

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图 8 基于RHRL和纯点预瞄方法的红旗E-HS3行驶路径

Fig. 8 Path of Hongqi E-HS3 vehicle controlled by RHRL and pure pursuit methods

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图 9 RHRL与纯点预瞄方法的车辆实测侧向偏差对比

Fig. 9 Comparison of experimental lateral tracking error of the RHRL and pure pursuit methods

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图 10 乡村砂石道路地图和车辆行驶中各阶段状态

Fig. 10 The route map in the country sand and gravel road, and the status of different stages in the control process

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图 11 侧向误差曲线

Fig. 11 Curves of the lateral error

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表 1 车辆动力学参数

Table 1 The parameters of the vehicle dynamics

符号	物理意义	数值	单位
$m$	车身质量	1723	kg
$I_z$	转动惯量	4175	kg·m²
$l_f$	质心到前轴距离	1.232	m
$l_r$	质心到后轴距离	1.468	m
$C_f$	前轮侧偏刚度	66900	N/rad
$C_r$	后轮侧偏刚度	62700	N/rad

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表 2 各控制器的均方根误差对比

Table 2 The RMSE comparison among all the controllers

方法	v_x = 30 km/h		v_x = 50 km/h
方法	e_y (m)	$e_\varphi$(rad)	e_y (m)	$e_\varphi $(rad)
RHRL	0.156	0.030	0.246	0.020
HDP	0.165	0.030	0.315	0.019
SAC	0.189	0.029	0.283	0.017
DDPG	0.172	0.037	0.319	0.017
MPC	0.212	0.025	0.278	0.015
纯点预瞄	0.159	0.036	0.286	0.030

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