单示范刀记忆截割BP神经网络自适应PID控制策略  

BP Neural Network Self-adaptive Control Strategy on Memory-cutting of Single Demonstration Cutter Shearer

作　　者：陈金国[1] 

机构地区：[1]莆田学院机电工程学院,福建莆田351100

出　　处：《煤矿机械》2015年第8期289-292,共4页Coal Mine Machinery

基　　金：福建省高校产学合作科技重大项目(2014H6021);国家自然科学基金资助项目(51074068)

摘　　要：为解决井下工作人员工作强度大和降低安全隐患,针对采煤机调高系统的非线性、滞后性及载荷时变的特点,根据采煤机滚筒调高系统原理,建立了采煤机单向示范刀记忆截割的数学模型,提出了BP神经网络自适应采煤机单向记忆截割控制策略,设计了采煤机BP神经网络自适应PID控制器。通过获取采煤机的姿态、位置和工作信息,结合了单向记忆截割数学原理,对采煤机记忆截割轨迹进行模拟仿真。结果表明:BP神经网络PID能够稳定、准确地跟踪示范刀顶板截割曲线,且跟踪吻合度好,满足工程实际要求。In order to reduce the work intensity and improve the safety of worker and overcome the feature of nonlinear and hysteresis of drum height adjusting system and the time variability of load, based on the principle of drum height adjusting shearer, establishing the model of the memory-cutting of single demonstration cutter shearer model. BP neural network self-adaptive control strategy on memory-cutting of single demonstration cutter is proposed and BP neural network self-adaptive controller is designed. The memory-cutting trajectory was simulated and tracked through obtaining the information of the shear＇s stance, position and working condition and Combining the mathematical principles of single demonstration cutter memory-cutting. The result shows that BP neural network self-adaptive PID can be stable and accurate tracking fit meet the engineer standards. cutting curves of demonstration knife,goodness of

关 键 词：采煤机 单向示范刀 记忆截割 BP神经网络自适应PID 

分 类 号：TD421.6[矿业工程—矿山机电] TP273[自动化与计算机技术—检测技术与自动化装置]