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基于无模型自适应控制的无人驾驶汽车横向控制方法

田涛涛 侯忠生 刘世达 邓志东

田涛涛, 侯忠生, 刘世达, 邓志东. 基于无模型自适应控制的无人驾驶汽车横向控制方法. 自动化学报, 2017, 43(11): 1931-1940. doi: 10.16383/j.aas.2017.c160633
引用本文: 田涛涛, 侯忠生, 刘世达, 邓志东. 基于无模型自适应控制的无人驾驶汽车横向控制方法. 自动化学报, 2017, 43(11): 1931-1940. doi: 10.16383/j.aas.2017.c160633
TIAN Tao-Tao, HOU Zhong-Sheng, LIU Shi-Da, DENG Zhi-Dong. Model-free Adaptive Control Based Lateral Control of Self-driving Car. ACTA AUTOMATICA SINICA, 2017, 43(11): 1931-1940. doi: 10.16383/j.aas.2017.c160633
Citation: TIAN Tao-Tao, HOU Zhong-Sheng, LIU Shi-Da, DENG Zhi-Dong. Model-free Adaptive Control Based Lateral Control of Self-driving Car. ACTA AUTOMATICA SINICA, 2017, 43(11): 1931-1940. doi: 10.16383/j.aas.2017.c160633

基于无模型自适应控制的无人驾驶汽车横向控制方法

doi: 10.16383/j.aas.2017.c160633
基金项目: 

国家自然科学基金 61433002

国家自然科学基金 61120106009

北京市自然科学基金——交控科技轨道交通联合基金 W17E000020

国家自然科学基金 91420106

详细信息
    作者简介:

    田涛涛 北京交通大学电子信息工程学院先进控制系统研究所硕士研究生.主要研究方向为大数据环境下的数据驱动控制和优化策略.E-mail:14120214@bjtu.edu.cn

    刘世达 北京交通大学电子信息工程学院先进控制系统研究所博士研究生.主要研究方向为数据驱动控制, 学习控制, 复杂工业过程.E-mail:lsdshiwo@hotmail.com

    邓志东 清华大学计算机系教授.主要研究方向为人工智能, 深度神经网络, 计算神经科学, 无人驾驶汽车, 先进机器人.E-mail:michael@tsinghua.edu.cn

    通讯作者:

    侯忠生 北京交通大学电子信息工程学院先进控制研究所教授.主要研究方向为无模型自适应控制理论, 数据驱动控制, 学习控制, 智能交通系统和数据挖掘在医学和交通领域中的应用.本文通信作者.E-mail:zhshhou@bjtu.edu.cn

Model-free Adaptive Control Based Lateral Control of Self-driving Car

Funds: 

National Natural Science Foundation of China 61433002

National Natural Science Foundation of China 61120106009

Beijing Natural Science Foundation Joint Fund for Science and Technology Rail Transportation W17E000020

National Natural Science Foundation of China 91420106

More Information
    Author Bio:

    Master student at the Advanced Control Systems Laboratory, School of Electronic and Information Engineering, Beijing Jiaotong University. His research interest covers data-driven control and optimal strategy under the environment of big data

    Ph. D. candidate at the Advanced Control Systems Laboratory, School of Electronic and Information Engineering, Beijing Jiaotong University. His research interest covers learning control, data-driven control, and complex industrial process

    Professor at the Department of Computer Science, Tsinghua University. His research interest covers artificial intelligence, deep neural network, computational neuroscience, driverless car, and advanced robotics

    Corresponding author: HOU Zhong-Sheng Professor at the Advanced Control Systems Laboratory, School of Electronic and Information Engineering, Beijing Jiaotong University. His research interest covers model free adaptive control, data-driven control, learning control, intelligent transportation system, and application of data mining in medical and traffic field. Corresponding author of this paper
  • 摘要: 提出了一种基于无模型自适应控制的无人驾驶汽车横向控制方案.首先,将无人驾驶汽车循迹跟踪控制问题转化成预瞄偏差角跟踪问题,然后基于无人驾驶汽车横向控制系统的动态线性化数据模型,设计出无模型自适应控制算法、伪梯度估计算法和伪梯度重置算法,进而实现了自主车辆的无人驾驶.该方法的实现仅用到无人驾驶汽车运行时的输入输出数据,避免了对无人驾驶汽车进行复杂机理建模的难题,对于复杂的无人驾驶汽车运行过程具有很好的自适应性,对不同的无人驾驶车辆具有较强的可移植性.该方案已实际应用于清华大学无人驾驶汽车实验平台,在北京市丰台区的实地测试实验、在江苏省常熟市高速路的测试以及2015年"中国智能车未来挑战赛"的现场应用验证了所提方案的有效性.
    1)  本文责任编委 魏庆来
  • 图  1  车载传感器分布图

    Fig.  1  Vehicle sensors distribution

    图  2  控制系统结构图

    Fig.  2  Control system structure

    图  3  预瞄点与预瞄距离示意图

    Fig.  3  Preview point and preview distance profile

    图  4  横向控制问题示意图

    Fig.  4  Schematic diagram of lateral control problem

    图  5  预瞄偏差角跟踪情况与汽车运行情况图

    Fig.  5  Preview-deviation-yaw tracking condition and self-driving car operation condition

    图  6  低速实验场地图

    Fig.  6  Low speed experimental site

    图  7  高速公路实验场地图

    Fig.  7  Highway experimental site

    图  8  设备连接关系及调试软件框架图

    Fig.  8  Equipment connection and debugging software framework

    图  9  两种控制方法的跟踪效果对比图

    Fig.  9  Tracking effect comparison between two control methods

    图  10  预瞄偏差角跟踪效果对比图

    Fig.  10  Preview-deviation-yaw tracking effect comparison

    图  11  跟踪误差绝对值对比图

    Fig.  11  Absolute value of tracking error comparison

    图  12  高速跟踪轨迹图

    Fig.  12  Highway trajectory tracking

    图  13  高速公路轨迹跟踪性能

    Fig.  13  Performance of highway trajectory tracking

    图  14  2015年“中国智能车未来挑战赛”部分项目

    Fig.  14  Several competition items at "2015 Chinese Intelligent Car Future Challenge"

    表  1  定位定姿和环境感知设备的作用

    Table  1  Navigation and environmental perception equipments

    常用设备 作用
    64线激光雷达感知周边环境信息
    4线激光雷达探测障碍物
    GPS天线定位以获取当前汽车位置信息
    黑白摄像头检测车道线
    彩色摄像头获取路口红绿灯、交通标志信息
    毫米波雷达检测汽车前方障碍物信息
    其他车载传感器检测汽车速度、汽车加速度等
    下载: 导出CSV

    表  2  常用概念符号意义

    Table  2  The meaning of common concepts and symbols

    图示符号 意义
    $ O$, $E$, $N$坐标原点、正东方、正北方
    $\alpha$汽车航向角(汽车运行方向与正北方向夹角)
    $\beta$预瞄点航向角(预瞄点运动方向与正北方向夹角)
    $l$预瞄距离
    $len$当前点和预瞄点之间的距离
    $LD$汽车预瞄点与汽车运行方向延长线的距离
    $AD$预瞄点航向角与汽车航向角的差
    $X$汽车当前点与预瞄点运行方向延长线的距离
    $\theta$当前点与预瞄点连线和汽车运行方向形成的角度
    下载: 导出CSV

    表  3  汽车状态数据符号及意义

    Table  3  Symbols and meanings of self-driving car0s state data

    符号 意义
    $x$, $y$, $\alpha$汽车当前点的横坐标、纵坐标、航向角
    $x^*$, $y^*$, $\beta$预瞄点的横坐标、纵坐标、航向角
    $v$汽车当前的纵向速度
    $a$汽车当前的纵向加速度
    $w$汽车当前点航向角的角速度
    下载: 导出CSV

    表  4  两种控制算法性能对比列表

    Table  4  The performance of two control algorithms

    项目 PID MFAC
    预瞄偏差角最大绝对值(rad)0.59450.4828
    预瞄偏差角均方根0.22590.1498
    跟踪误差的均方根0.52570.3320
    建立时间(s)85
    下载: 导出CSV
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  • 收稿日期:  2016-09-05
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