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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
  • [1] 高昌平, 毕仕强, 蔡沈卫. 世界汽车工业的崛起与发展研究[J] . 产业与科技论坛,2020,19(15):77 − 78. doi: 10.3969/j.issn.1673-5641.2020.15.038
    [2] 陈明伟, 袁晓华, 潘敏, 等. 从道路交通事故统计分析对比谈预防措施[J] . 中国安全科学学报,2004(8):62 − 66.
    [3] HUANG Y J, DING H T, ZHAG Y B, et al. A motion planning and tracking framework for autonomous vehicles based on artificial potential field elaborated resistance network approach[J] . IEEE Transactions on Industrial Electronics,2020,67(2):1376 − 1386. doi: 10.1109/TIE.2019.2898599
    [4] TALVALAK L R, KRITAYAKIRANA K, GERDES J C. Pushing the limits: From lanekeeping to autonomous racing[J] . Annual Reviews in Control,2011,35(1):137 − 148. doi: 10.1016/j.arcontrol.2011.03.009
    [5] YAO Q Q, TIAN Y, WANG Q, et al. Control strategies on path tracking for autonomous vehicle: State of the art and future challenges[J] . IEEE ACCESS,2020,8:161211 − 161222. doi: 10.1109/ACCESS.2020.3020075
    [6] MARINO R, SCALZI S, NETTO M. Nested PID steering control for lane keeping in autonomous vehicles[J] . Control Engineering Practice,2011,19(12):1459 − 1467. doi: 10.1016/j.conengprac.2011.08.005
    [7] 王占山, 王继东, 刘秀翀, 等. 二阶系统的复特征根对其鲁棒H控制性能的影响[J] . 东北大学学报(自然科学版),2019,40(9):1217 − 1221.
    [8] WANG P W, GAO S, LI L, et al. Automatic steering control strategy for unmanned vehicles based on robust backstepping sliding mode control theory[J] . IEEE ACCESS,2019,7:64984 − 64992. doi: 10.1109/ACCESS.2019.2917507
    [9] 邓国红, 肖皓鑫, 韩龙海, 等. 基于模型预测控制的车辆横纵向跟踪控制[J] . 重庆理工大学学报(自然科学),2021,35(11):18 − 26.
    [10] 刘子龙, 杨汝清, 杨明, 等. 无人驾驶车辆横向位置最优跟踪控制[J] . 上海交通大学学报,2008,42(2):257 − 261, 265.
    [11] GOODRAZI A, SABOOTEH A, ESMAILZADEH E. Automatic path control based on integrated steering and external yaw-moment control[J] . Proceedings of the Institution of Mechanical Engineers Part K: Journal of Multi-body Dynamics,2008,222(2):189 − 200. doi: 10.1243/14644193JMBD120
    [12] 高琳琳, 唐风敏, 郭蓬, 等. 自动驾驶横向运动控制的改进LQR方法研究[J] . 机械科学与技术,2021,40(3):435 − 441.
    [13] 倪兰青, 林棻. 基于预瞄的智能车辆路径跟踪控制研究[J] . 重庆理工大学学报(自然科学版),2017,31(3):27 − 33.
    [14] YANG T, BAI Z, LI Z, et al. Intelligent vehicle lateral control method based on feedforward + predictive LQR algorithm[J] . Actuators,2021,10(9):228. doi: 10.3390/act10090228
    [15] PARK M, KANG Y. Experimental verification of a drift controller for autonomous vehicle tracking: A circular trajectory using LQR method[J] . International Journal of Control, Automation and Systems,2020,19(1):404 − 416.
    [16] YUE M, AN C, SUN J. Zero dynamics stabilisation and adaptive trajectory tracking for WIP vehicles through feedback linearisation and LQR technique[J] . International Journal of Control,2016,89(12):2533 − 2542. doi: 10.1080/00207179.2016.1169440
    [17] YU H, ZHAO C, LI S, et al. Pre-work for the birth of driver-less scraper (LHD) in the underground mine: The path tracking control based on an LQR controller and algorithms comparison[J] . Sensors (Basel),2021,21(23):7839. doi: 10.3390/s21237839
    [18] LICHOTA P, DUL F, KARBOWSKI A. System identification and LQR controller design with incomplete state observation for aircraft trajectory tracking[J] . Energies,2020,13(20):5354. doi: 10.3390/en13205354
    [19] SINELKHATEM A, NACI E S. Robust LQR and LQR-PI control strategies based on adaptive weighting matrix selection for a UAV position and attitude tracking control[J] . Alexandria Engineering Journal,2022,61(8):6275 − 6292. doi: 10.1016/j.aej.2021.11.057
    [20] 梁忠超, 张欢, 赵晶, 等. 基于自适应MPC的无人驾驶车辆轨迹跟踪控制[J] . 东北大学学报(自然科学版),2020,41(6):835 − 840. doi: 10.12068/j.issn.1005-3026.2020.06.013
    [21] 孙忠廷, 柏建军, 陈炳旭, 等. 轮式移动机器人自适应轨迹跟踪控制[J] . 控制工程,2021,28(12):2420 − 2425.
    [22] REN Y, ZHAO Z, K C, et al. Adaptive fuzzy control for an uncertain axially moving slung-load cable system of a hovering helicopter with actuator fault[J] . IEEE Transactions on Fuzzy Systems,2022,61(8):6275 − 6292.
    [23] WU Y X, HUANG R, LI X, et al. Adaptive neural network control of uncertain robotic manipulators with external disturbance and time-varying output constraints[J] . Neurocomputing,2019,323:108 − 116. doi: 10.1016/j.neucom.2018.09.072
    [24] LIU L, LIU Y J, LI D, et al. Barrier lyapunov function-based adaptive fuzzy FTC for switched systems and its applications to resistance-inductance-capacitance circuit system[J] . IEEE Transactions on Cybernetics,2020,50(8):3491 − 3502. doi: 10.1109/TCYB.2019.2931770
    [25] 杨亮, 陈勇, 刘治. 基于参数不确定机械臂系统的自适应轨迹跟踪控制[J] . 控制与决策,2019,34(11):2485 − 2490.
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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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