露天矿无人驾驶自卸车横−纵向协同控制研究

潘国宇, 鲍久圣, 胡德平, 邹学耀, 阴妍, 王茂森, 朱晨钟, 张磊, 杨瑞

潘国宇,鲍久圣,胡德平,等. 露天矿无人驾驶自卸车横−纵向协同控制研究[J]. 工矿自动化,2024,50(10):68-79. DOI: 10.13272/j.issn.1671-251x.2024070017
引用本文: 潘国宇,鲍久圣,胡德平,等. 露天矿无人驾驶自卸车横−纵向协同控制研究[J]. 工矿自动化,2024,50(10):68-79. DOI: 10.13272/j.issn.1671-251x.2024070017
PAN Guoyu, BAO Jiusheng, HU Deping, et al. Research on lateral-longitudinal coordinated control of unmanned dump trucks in open-pit mine[J]. Journal of Mine Automation,2024,50(10):68-79. DOI: 10.13272/j.issn.1671-251x.2024070017
Citation: PAN Guoyu, BAO Jiusheng, HU Deping, et al. Research on lateral-longitudinal coordinated control of unmanned dump trucks in open-pit mine[J]. Journal of Mine Automation,2024,50(10):68-79. DOI: 10.13272/j.issn.1671-251x.2024070017

露天矿无人驾驶自卸车横−纵向协同控制研究

基金项目: 江苏省科技成果转化专项资金项目(BA2023035);徐州市重点研发计划资助项目(KC22419);江苏高校优势学科建设工程资助项目(PAPD)。
详细信息
    作者简介:

    潘国宇(1999—),男,江苏盐城人,硕士研究生,研究方向为矿山无人驾驶,E-mail:2116731830@qq.com

    通讯作者:

    鲍久圣(1979—),男,安徽桐城人,教授,博士,博士研究生导师,研究方向为矿山运输及其智能化,E-mail: cumtbjs@cumt.edu.cn

  • 中图分类号: TD57

Research on lateral-longitudinal coordinated control of unmanned dump trucks in open-pit mine

  • 摘要: 露天矿无人驾驶自卸车面临道路等级低且坡道弯道多、车辆载质量大且变化范围宽等恶劣运输工况,现有车辆运动控制策略多面向普通道路环境,无法直接将现有车辆控制策略应用于矿山自卸车。针对上述问题,提出了一种基于预瞄误差与分层反馈的露天矿无人驾驶自卸车横−纵向协同控制系统。横向控制以线性二次型调节器(LQR)为基础,运用前馈控制器降低稳态误差,采用模糊控制器实现自适应调整预瞄距离,以提高路径跟踪控制精度;纵向控制建立分层反馈式纵向速度控制器,分别采用模型预测控制和模糊PID反馈控制,并建立车辆驱动及制动逆向模型,降低自卸车载质量与道路坡度改变对纵向速度追踪的影响。仿真结果表明:① 车辆实际速度和期望速度误差在2%以内,说明自卸车在空载下坡与满载上坡2种工况下的速度跟踪效果能够满足要求。② 由于横−纵向协同控制能够针对路径曲率的不同实时调节车辆速度,在2种工况下,自卸车横−纵向协同控制器相比于单一横向控制都获得了更高的路径跟踪精度,同时也提高了自卸车的操纵稳定性。实验结果表明:① 空载下坡时的横向误差峰值为0.019 9 m,方向误差峰值为0.184 0 rad,误差增大均发生在弯道处,但误差波动范围较小,能够保证试验车对期望路径的跟踪。② 负载上坡时的横向误差峰值为0.016 8 m,方向误差峰值为0.071 4 rad,误差变化趋势与空载下坡试验相反,但误差仍在合理范围内,试验车的跟踪效果良好。③ 2个误差峰值均小于空载下坡试验,验证了不同速度对横向控制精度的影响规律。
    Abstract: Open-pit mine unmanned dump trucks face harsh transportation conditions, such as low-grade roads with numerous ramps and curves, as well as heavy and highly variable loads. Most existing vehicle motion control strategies are designed for conventional road environments, making them unsuitable for direct application to mine dump trucks. To address these issues, a lateral-longitudinal coordinated control system based on preview error and layered feedback was proposed for unmanned open-pit mine dump trucks. The lateral control was based on a linear quadratic regulator (LQR) and employed a feedforward controller to reduce steady-state errors, while a fuzzy controller was used to adaptively adjust the preview distance, thereby improving path tracking accuracy. The longitudinal control established a layered feedback longitudinal speed controller, which used model predictive control and fuzzy proportional-integral-differential (PID) feedback control. In addition, an inverse model for vehicle driving and braking was established to minimize the impact of load and road gradient changes on longitudinal speed tracking. Simulation results indicated that: ① The error between the actual speed and the desired speed was within 2%, demonstrating that the speed tracking performance of the dump truck could meet requirements under both empty downhill and fully loaded uphill conditions. ② Due to the lateral-longitudinal coordinated control’s ability to adjust vehicle speed in real time based on varying road curvature, the coordinated controller achieved higher path tracking accuracy compared to single lateral control in both operating conditions, while also enhancing vehicle maneuverability and stability. Laboratory test results showed that: ① The peak lateral error during empty downhill runs was 0.0199 m, and the peak direction error was 0.1840 rad. Both errors increased at curves, but their fluctuations were minimal, ensuring that the test vehicle effectively tracked the desired path. ② During loaded uphill runs, the peak lateral error was 0.0168 m, and the peak direction error was 0.0714 rad. The error trends were opposite to those observed in empty downhill tests, but the errors remained within acceptable limits, resulting in good path tracking performance. ③ Both peak errors were lower compared to those in empty downhill tests, which validated the effect of varying speeds on lateral control accuracy.
  • 图  1   横向动力学模型

    Figure  1.   Lateral kinetic model

    图  2   纵向动力学模型

    Figure  2.   Longitudinal kinetic model

    图  3   横−纵向协同控制系统

    Figure  3.   Lateral-longitudinal coordinated control system

    图  4   路径跟踪预瞄误差模型

    Figure  4.   Path tracking preview error model

    图  5   隶属函数

    Figure  5.   Membership function

    图  6   自适应模糊控制器输入输出关系曲面

    Figure  6.   Surface plot of input-output relationship for adaptive fuzzy controller

    图  7   驱动/制动切换逻辑曲线

    Figure  7.   Logic curves of drive/brake switching

    图  8   露天矿自卸车基本工况

    Figure  8.   Basic working condition of open-pit mine dump truck

    图  9   回返式露天矿仿真道路

    Figure  9.   Simulation road of return-type open-pit mine

    图  10   露天矿自卸车期望速度曲线

    Figure  10.   Expected velocity curve of open-pit mine dump truck

    图  11   横−纵向协同控制器联合仿真模型

    Figure  11.   Co-simulation model of latera-longitudinal coordinated controller

    图  12   2种工况下期望速度跟踪误差曲线

    Figure  12.   Expected speed tracking error curve under two working conditions

    图  13   空载下坡工况路径跟踪主要参数仿真结果

    Figure  13.   Simulation results of main parameters for path tracking under no-load downhill condition

    图  14   满载上坡工况路径跟踪主要参数仿真结果

    Figure  14.   Simulation results of main parameters for path tracking under full load uphill condition

    图  15   无人驾驶自卸车模型试验车组成

    Figure  15.   Composition of unmanned dump truck model test truck

    图  16   设计的露天矿模拟试验道路

    Figure  16.   Open-pit mine simulation test road

    图  17   模拟试验道路模型

    Figure  17.   Simulation test road model

    图  18   激光雷达生成的栅格地图

    Figure  18.   LiDAR-generated raster map

    图  19   ROS中生成的规划路径

    Figure  19.   Planning path generated in ROS

    图  20   空载下坡模拟试验结果

    Figure  20.   Results of no-load downhill simulation test

    图  21   负载上坡模拟试验结果

    Figure  21.   Results of load uphill simulation test

    表  1   模糊控制规则

    Table  1   Fuzzy control rules

    vx k
    k1 k2 k3 k4 k5 k6 k7
    v1 l3 l2 l2 l2 l1 l1 l1
    v2 l4 l4 l3 l3 l2 l2 l1
    v3 l5 l4 l4 l3 l3 l2 l2
    v4 l6 l5 l5 l4 l4 l3 l3
    v5 l6 l6 l5 l5 l4 l4 l3
    v6 l7 l6 l6 l5 l5 l4 l4
    v7 l7 l7 l6 l6 l5 l5 l4
    下载: 导出CSV

    表  2   自卸车仿真参数

    Table  2   Simulation parameters of dump truck

    车辆参数数值
    车辆质量/kg30000
    额定载质量/kg60000
    发动机功率/rpm338/2100
    质心距前轴的距离/m1.385
    质心距前轴的距离/m3.75
    下载: 导出CSV

    表  3   模型试验车基本参数

    Table  3   Basic parameters of model test vehicle

    类型 参数名称 数值
    车辆参数 尺寸/mm×mm×mm 240.5×191×146
    轮距/m 0.16
    轴距/m 0.142
    整车质量/kg 1.8
    驱动电动机 额定功率/W 4.32
    额定扭矩/(kg·m) 1
    舵机 最大角度/(°) 180
    扭矩/(kg·cm) 15.3~20
    惯性测量单元 陀螺仪量程/((°)·s−1 ±150/500/1 000/2 000
    加速度量程 ±2g/4g/8g/16g
    罗盘量程/μT ±4 800
    单线激光雷达 扫描角度/(°) 0~360
    扫描频率/Hz 5.5
    主控制器 CPU ARM Cortex A57 64 bit@1.43 GHz
    GPU 128−core Maxwell@921 MHz
    内存/(GiB·s−1) 4 GiB 64 bit LPDDR4 |25.6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-07-04
  • 修回日期:  2024-10-30
  • 网络出版日期:  2024-11-07
  • 刊出日期:  2024-10-24

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