基于混合人群搜索算法的采煤机调高系统控制研究  

作　　者：陈智星 王李进 陈鹏飞 徐立新 

机构地区：[1]华晋焦煤有限责任公司沙曲二号煤矿,山西吕梁033000 [2]东平湖管理局供水局陈垓闸管理所,山东泰安271599

出　　处：《科技创新与应用》2024年第13期68-72,共5页Technology Innovation and Application

基　　金：山西省科技厅一般科研课题(202102100401015)。

摘　　要：为提高煤矿井下采煤机控制稳定性,针对采煤机在工作过程中调高精度不足,响应速度较慢的问题,利用人群搜索算法(SOA)对其进行参数整定,提高其控制效果。同时针对人群搜索算法存在的后期个体大量聚集、无法利用有效信息、搜索速度不足以及陷入局部最优的问题,提出一种混合灰狼算法领导者与追随者思想、莱维飞行策略、混沌映射策略的改进人群搜索算法整定采煤机调高控制系统参数的控制策略。使用改进前后的人群搜索算法以及粒子群搜索算法,分别采用MATLAB仿真及采煤机控制实验,综合考虑超调量、上升时间、调整时间,得出结论,即改进后的SOA算法收敛速度提高2 s、综合搜索精度提高,且整定后的控制参数使得采煤机调高系统具有更快的响应速度,更高的平衡性。可以为未来采煤机高效开采提供一定的理论依据。In order to improve the control stability of the underground shearer in the coal mine,aiming at the problem of insufficient adjustment precision and slow response speed of the shearer in the working process,the Seeker Optimization Algorithm(SOA)is used to adjust its parameters to improve its control effect.At the same time,aiming at the problems existing in the Seeker Optimization Algorithm,such as a large number of individuals gathering in the later stage,unable to make use of effective information,insufficient search speed,and falling into local optimization,this paper presents an improved Seeker Optimization Algorithm based on the idea of leader and follower of gray wolf algorithm,Levy flight strategy and chaos mapping strategy to set the parameters of shearer heightening control system.Using the improved Seeker Optimization Algorithm and particle swarm search algorithm,using MATLAB simulation and shearer control experiments respectively,comprehensively considering the overshoot,rising time and adjustment time,it is concluded that the convergence speed of the improved SOA algorithm is improved by 2 s,the comprehensive search accuracy is improved,and the adjusted control parameters make the shearer height adjustment system have faster response speed and higher balance.It can provide a certain theoretical basis for efficient mining of shearer in the future.

关 键 词：采煤机 参数整定 人群搜索算法 自动调高 灰狼算法 

分 类 号：TD421[矿业工程—矿山机电]