基于LQR-QPSO的地下铲运机控制参数优化研究

doi:10.11872/j.issn.1005-2518.2021.01.167

Abstract

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

CLC Number:

TD273

Yongchun LIU,Liguan WANG,Jiaxi WU. Optimization of Control Parameters for Underground Load-Haul-Dump Machine Based on LQR-QPSO[J].Gold Science and Technology, 2021, 29(1): 25-34.

Figures/Tables 13

Fig.1

Fig.2

Fig.3

Table 1

Body geometry parameter of underground load-haul-dump unit"

参数名称	数值
前桥至铰接点距离 $L f / m$	1.755
后桥至铰接点距离 $L r / m$	1.855
车身宽度 $W / m$	2.26
轮胎直径 $d / m$	1.30
最大满载速度 $v m a x / (m · s - 1)$	6.92
铰接转向角范围 $γ / r a d$	0.25π
最大铰接转向角速度 $γ ˙ m a x / (r a d · s - 1)$	0.15

Table 1

Fig.4

Fig.5

Fig.6

Table 2

Initial parameter setting of the particle swarm optimization algorithm and its improved algorithm"

参数名称	数值	参数名称	数值
种群规模 $N$	50.0	学习因子 $c 2$	2.0
迭代次数 $G$	100.0	最大移动速度 $v m a x$	1.0
初始惯性权重 $w i n i$	0.9	粒子取值极限 $q m a x$	500.0
终止惯性权重 $w e n d$	0.4	初始收缩—扩张系数 $α i n i$	1.0
学习因子 $c 1$	2.0	终止收缩—扩张系数 $α e n d$	0.5

Table 2

Table 3

Parameter optimization results of the particle swarm optimization algorithm and its improved algorithm"

算法	加权矩阵Q			线性反馈矩阵K			适应度
算法	$q 1$	$q 2$	$q 3$	$k 1$	$k 2$	$k 3$	适应度
PSO	0.4560	89.9876	61.3431	0.6753	10.2855	11.7014	19.5232
QPSO	0.0142	55.8160	40.3936	0.1193	7.6420	10.8306	4.5278

Table 3

Fig.7

Fig.8

Fig.9

Fig.10

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

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