收稿日期: 2020-09-17
修回日期: 2020-10-26
网络出版日期: 2021-03-22
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
Received date: 2020-09-17
Revised date: 2020-10-26
Online published: 2021-03-22
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。
刘永春 , 王李管 , 吴家希 . 基于LQR-QPSO的地下铲运机控制参数优化研究[J]. 黄金科学技术, 2021 , 29(1) : 25 -34 . DOI: 10.11872/j.issn.1005-2518.2021.01.167
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.
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