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基于递归最小二乘的智能车轨迹跟踪控制算法

李腾 任洪娟

李腾, 任洪娟. 基于递归最小二乘的智能车轨迹跟踪控制算法[J]. 上海工程技术大学学报, 2023, 37(4): 372-379, 408. doi: 10.12299/jsues.22-0209
引用本文: 李腾, 任洪娟. 基于递归最小二乘的智能车轨迹跟踪控制算法[J]. 上海工程技术大学学报, 2023, 37(4): 372-379, 408. doi: 10.12299/jsues.22-0209
LI Teng, REN Hongjuan. Intelligent vehicle trajectory tracking control algorithm based on recursive least square[J]. Journal of Shanghai University of Engineering Science, 2023, 37(4): 372-379, 408. doi: 10.12299/jsues.22-0209
Citation: LI Teng, REN Hongjuan. Intelligent vehicle trajectory tracking control algorithm based on recursive least square[J]. Journal of Shanghai University of Engineering Science, 2023, 37(4): 372-379, 408. doi: 10.12299/jsues.22-0209

基于递归最小二乘的智能车轨迹跟踪控制算法

doi: 10.12299/jsues.22-0209
详细信息
    作者简介:

    李腾:李 腾(1997−),男,在读硕士,研究方向为智能车轨迹跟踪控制. E-mail:15628957598@163.com

    通讯作者:

    任洪娟(1978−),女,副教授,博士,研究方向为汽车动力系统设计与节能减排. E-mail:ren-hongjuan@163.com

  • 中图分类号: U461.1

Intelligent vehicle trajectory tracking control algorithm based on recursive least square

  • 摘要: 提出一种实时估计轮胎侧偏刚度的自适应横向最优跟踪控制算法. 路面附着系数的改变使得轮胎侧向力的近似线性区间发生改变,线性近似得到的轮胎侧偏刚度将不再可靠. 基于递归最小二乘算法,以轮胎侧偏角和侧向力作为输入,实时在线估计轮胎的侧偏刚度,进而提出自适应线性二次型调节器(Adaptive Linear Quadratic Regulator,ALQR)控制器. 在Matlab/Simulink和Carsim联合仿真平台上对其有效性和稳健性进行验证. 结果表明,在多种路面附着条件和不同车速下,所设计的控制算法的性能均优于传统线性二次型调节器(Linear Quadratic Regulator,LQR)控制算法,最大横向位置误差和横摆角误差分别降低81.5%和73.0%. 通过实车测试,算法的实际应用性和有效性得到实证,最大轨迹跟踪误差仅为0.56 m.
  • 图  1  车辆动力学模型

    Figure  1.  Vehicle dynamics model

    图  2  轨迹跟踪模型

    Figure  2.  Trajectory tracking model

    图  3  不同附着系数下侧偏角与侧向力的关系

    Figure  3.  Relationship between lateral deflection angle and lateral force under different adhesion coefficients

    图  4  ALQR控制器控制流程

    Figure  4.  ALQR controller control process

    图  5  高附着工况72 km/h控制效果对比

    Figure  5.  Comparison of 72 km/h control effect under high adhesion condition

    图  6  低附着工况90 km/h控制效果对比

    Figure  6.  Comparison of 90 km/h control effect under low adhesion condition

    图  7  智能驾驶车辆

    Figure  7.  Intelligent driving vehicle

    图  8  智能驾驶车辆平台硬件系统

    Figure  8.  Intelligent driving vehicle platform hardware system

    图  9  实车试验跟踪效果

    Figure  9.  Tracking effect of real vehicle test

    表  1  整车参数

    Table  1.   Vehicle parameters

    参数数值
    整车质量m/kg
    轴距l/m
    汽车的转动惯量Iz/( kg∙m2
    质心到前轴的距离a/m
    质心到后轴的距离b/m
    1865
    2.7
    4175
    1.232
    1.468
    下载: 导出CSV

    表  2  车速72 km/h跟踪误差

    Table  2.   Vehicle speed 72 km/h tracking error

    控制器峰值横向位置
    误差/m
    峰值横摆角
    误差/(°)
    ALQR0.05170.2235
    LQR0.05590.2340
    误差降低/%
    7.54.5
    下载: 导出CSV

    表  3  低附着工况跟踪误差

    Table  3.   Tracking error under low adhesion condition

    控制器峰值横向位置
    误差/m
    峰值横摆角
    误差/(°)
    ALQR 0.5045 1.9675
    LQR 2.7265 7.2911
    误差降低/%
    81.5 73.0
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-07-09
  • 刊出日期:  2023-12-30

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