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黄金科学技术 ›› 2021, Vol. 29 ›› Issue (1): 25-34.doi: 10.11872/j.issn.1005-2518.2021.01.167

• 智慧矿山专栏 • 上一篇    下一篇

基于LQR-QPSO的地下铲运机控制参数优化研究

刘永春1,2(),王李管1,2(),吴家希1,2   

  1. 1.中南大学资源与安全工程学院,湖南 长沙 410083
    2.中南大学数字矿山研究中心,湖南 长沙 410083
  • 收稿日期:2020-09-17 修回日期:2020-10-26 出版日期:2021-02-28 发布日期:2021-03-22
  • 通讯作者: 王李管 E-mail:liuyongchun@csu.edu.cn;liguan_wang@163.com
  • 作者简介:刘永春(1996-),男,湖南衡阳人,硕士研究生,从事智能采矿与装备方面的研究工作。liuyongchun@csu.edu.cn
  • 基金资助:
    国家重点研发计划项目“深部集约化开采生产过程智能管控技术”(2017YFC0602905);“井下人机定位和作业环境感知分析技术与系统”(2018SK2053)

Optimization of Control Parameters for Underground Load-Haul-Dump Machine Based on LQR-QPSO

Yongchun LIU1,2(),Liguan WANG1,2(),Jiaxi WU1,2   

  1. 1.School of Resources and Safety Engineering,Central South University,Changsha 410083,Hunan,China
    2.Digital Mine Research Center,Central South University,Changsha 410083,Hunan,China
  • Received:2020-09-17 Revised:2020-10-26 Online:2021-02-28 Published:2021-03-22
  • Contact: Liguan WANG E-mail:liuyongchun@csu.edu.cn;liguan_wang@163.com

摘要:

现代控制理论是实现地下铲运机路径跟踪控制的重要技术之一。目前,控制算法应用的难点在于参数的选取和整定。为解决控制参数整定问题,提出应用量子行为粒子群优化算法(QPSO)对基于线性二次型调节(LQR)的状态反馈控制器进行参数优化,实现对地下铲运机精准、稳定的路径跟踪控制。状态反馈控制器基于铲运机的误差动力学模型得出,优化后的路径跟踪控制最大横向位置偏差低于0.23 m。仿真试验结果表明:相较于标准粒子群优化算法,QPSO算法优化的路径跟踪控制器的最大横向位置偏差减小53.4%,优化效果更好、成功率更高。

关键词: 地下铲运机, 路径跟踪控制, 控制参数优化, 粒子群优化算法, 量子行为粒子群优化算法, 线性二次型调节器

Abstract:

With the increase of mining depth,the mining operation environment is worse and worse.It is of great significance to realize the underground unmanned load-haul-dump(LHD) machine to ensure the safe and efficient production of mine enterprises.In underground operation,the long,low articulated body of under-ground LHD machine has the characteristics of high mass,high inertia and high steering delay,which makes the precise tracking of the scraper path a difficult point for its realization of unmanned driving.As an important technique of path tracing control,the control algorithm based on optimization principle often has the problem of parameter selection and adjustment.In industrial applications,manual trial-and-error methods are commonly used for parameter selection.This method not only consumes a lot of human and time costs,but also makes it difficult to ensure the accuracy because of the lack of relevant experience of the operator.In this paper,the method of parameter optimization for linear quadratic regulator(LQR) state feedback controller by quantum-behaved particle swarm optimization(QPSO) algorithm was proposed.The LQR state feedback controller was cons-tructed based on error dynamics model.After parameter optimization,the maximum lateral error of path tracking is not more than 0.23 m.In a large number of repeated experiments,it is found that the standard particle swarm optimization(PSO) algorithm is difficult to find the proper parameter that can make the controller cross deviation lower than 0.5 m in 100 iterations.The QPSO algorithm has found the optimal parameter which meets the condition in the 10 repeated experiments.In 100 iterations,the fitness of the PSO algorithm tends to converge at 21 iterations,while that of the QPSO algorithm converges to a lower level than that of the PSO in the seventh iteration.The maximal lateral position deviation of the path tracking controller is reduced by 53.4%.It can be seen that the parameter optimization ability of the QPSO algorithm is obviously stronger than that of the PSO algorithm.The QPSO algorithm has faster optimization speed and higher success rate than the PSO algorithm.The control parameters of the LQR state feedback controller are automated by the QPSO algorithm.The design and parameter tuning process of the entire path tracking controller has important reference significance for realizing the unmanned driving of underground LHD machine.

Key words: underground load-haul-dump machine, path tracking control, optimization of control parameters, particle swarm optimization, quantum particle swarm optimization, linear quadratic regulator

中图分类号: 

  • TD273

图1

地下铲运机稳态转向与原地转向模型"

图2

地下铲运机运动轨迹示意图注:Pe、P为期望瞬时期望参考点和实际定位参考点;曲线A、B为期望路径和实际路径;Oe、O为期望转向中心和实际转向中心;ve、v为期望速度和实际速度;θe、θf为期望航向角和实际航向角"

图3

地下铲运机误差动力学模型示意图注:Pe、P为期望瞬时期望参考点和实际定位参考点;Pe'、P'为下一时刻期望瞬时期望参考点和实际定位参考点;?Oe、O为短时间内期望转向中心和实际转向中心;εθ、εd为航向角偏差与横向位置偏差;εθ'、εd'为下一时刻的航向角偏差与横向位置偏差;dθ、dεd为航向角变化和横向位置偏差变化"

表1

地下铲运机车身几何参数"

参数名称数值
前桥至铰接点距离Lf/m1.755
后桥至铰接点距离Lr/m1.855
车身宽度W/m2.26
轮胎直径d/m1.30
最大满载速度vmax/(m·s-1)6.92
铰接转向角范围γ/rad0.25π
最大铰接转向角速度γ˙max/(rad·s-1)0.15

图4

粒子群优化算法流程图"

图5

LQR-QPSO路径跟踪控制器"

图6

试验参考巷道三维激光点云地图"

表2

粒子群优化算法及其改进算法的初始参数设置"

参数名称数值参数名称数值
种群规模N50.0学习因子c22.0
迭代次数G100.0最大移动速度vmax1.0
初始惯性权重wini0.9粒子取值极限qmax500.0
终止惯性权重wend0.4初始收缩—扩张系数αini1.0
学习因子c12.0终止收缩—扩张系数αend0.5

表3

粒子群算法及其改进算法的参数优化结果"

算法加权矩阵Q线性反馈矩阵K适应度
q1q2q3k1k2k3
PSO0.456089.987661.34310.675310.285511.701419.5232
QPSO0.014255.816040.39360.11937.642010.83064.5278

图7

历史最优适应度"

图8

不同优化参数行驶路径对比"

图9

不同优化参数控制偏差对比"

图10

不同优化参数控制输出对比"

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