基于PSO优化的悬臂式掘进机自适应控制研究  

Research on Adaptive Control of Cantilever Type Roadheader Based on PSO Optimization

作　　者：许吉禅 阮大志 马天兵[2,3] 杨孝筱 Xu Jichan;Ruan Dazhi;Ma Tianbing;Yang Xiaoxiao(Collaborative Innovation Center for Mine Intelligent Technology and Equipment,Huainan 232001,China;School of Mechanical and Electrical Engineering,Anhui University of Science and Technology,Huainan 232001,China;State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines,Huainan 232001,China)

机构地区：[1]矿山智能技术与装备省部共建协同创新中心,安徽淮南232001 [2]安徽理工大学机电工程学院,安徽淮南232001 [3]深部煤矿采动响应与灾害防控国家重点实验室,安徽淮南232001

出　　处：《煤矿机械》2024年第11期150-153,共4页Coal Mine Machinery

基　　金：安徽理工大学矿山智能技术与装备省部协同创新中心开放基金项目(CICJMITE202205);安徽省高校协同创新项目(GXXT-2022-019);安徽省重点研究与开发计划项目(202104a07020005)。

摘　　要：针对悬臂式掘进机手动控制操作时掘进效率低、设备损耗大等问题,提出一种基于神经网络的自适应控制策略,通过粒子群优化(PSO)算法优化极限学习机(ELM)负载识别和模糊PID控制,实现截割负载识别和摆速控制。基于Simulink的仿真结果显示,PSO-ELM识别系统的平均绝对误差百分比小于0.3%,PSO-FPID摆速控制时间小于0.2 s,验证了该策略的有效性,对提高煤炭开采的智能化水平具有重要意义。Aiming at the problems of low digging efficiency and high equipment loss during manual control operation of cantilever type roadheader,proposed an adaptive control strategy based on neural network to realize the cutting load identification and swing speed control through particle swalm optimization(PSO)algorithm optimizing extreme learning machine(ELM)load identification and fuzzy PID control.Simulation results based on Simulink show that the mean absolute percentage error of PSO-ELM identification system is less than 0.3%,and the PSO-FPID swing speed control time is less than 0.2 seconds,which verifies the effectiveness of the strategy and is of great significance to improve the intelligent level of coal mining.

关 键 词：悬臂式掘进机 自适应控制策略 PSO ELM 模糊PID 

分 类 号：TD421.5[矿业工程—矿山机电]