基于神经网络与遗传算法的悬臂式掘进机智能控制系统  

The boom- type roadheader intelligent control system besed on neural network and genetic algorithms

作　　者：刘若涵[1] 赵振民[1] 赵杰[1] 

机构地区：[1]黑龙江科技大学电气与控制工程学院,哈尔滨150022

出　　处：《工业仪表与自动化装置》2014年第6期67-69,共3页Industrial Instrumentation & Automation

基　　金：国家自然科学基金项目(51304075);黑龙江省教育厅面上项目(12541706);黑龙江省研究生创新项目(YJSCX2014-109HKD)

摘　　要：由于煤矿巷道内煤层的分布是不均匀的,并且是时时变化的,这就给高效开采带来了难度。悬臂式掘进机在巷道中作业时,会因为煤层分布的不确定性,导致所受的截割阻力不停地变化,因此,要求掘进机可以根据不同的工况,能够快速地、实时地调整悬臂的摆动速度。该文提出了基于神经网络与遗传算法的复合控制理论的悬臂式掘进机的控制模式。该控制系统能有效地提高生产效率,并且安全可靠。Since the distribution of the coal seams is uneven and continual variation, it brings diffi-culty to excavate efficiently.When boom-type roadheader is working in the tunnel, because of the uncer-tainty of the distribution of coal seams, cutting resistance suffered by roadheader is changing constantly. So the roadheader should adjust boom swing speed quickly and in real time according to different condi-tions.This paper presents a composite control mode, that is a control theory of boom-type roadheader based on neural network and genetic algorithms, which can improve production efficiency effectively, and safety and reliability.

关 键 词：神经网络 遗传算法 悬臂式掘进机 智能控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]