工矿自动化2025，Vol.51Issue(1)：61-70,137,11.DOI:10.13272/j.issn.1671-251x.2024070084
基于卷积神经网络和模糊PID的掘进机截割控制系统研究
Research on the roadheader cutting control system based on convolutional neural network and fuzzy PID
摘要
Abstract
In response to the insufficient adaptability and low system stability of cantilever roadheader when facing changes in coal and rock hardness during tunneling,a roadheader cutting control system based on convolutional neural networks(CNN)and fuzzy PID is proposed.This includes two parts:the cross-section forming characteristics of the tunnel and the intelligent cutting control strategy.The intelligent roadheader cutting control strategy consists of a CNN coal rock hardness dynamic perception module and a cutting arm swing speed fuzzy PID control module.An effective cutting path is proposed to make the cutting head cut coal and rock top to bottom along the planned path,aiming to improve the integrity of the cross-section and reduce the error in the tunneling direction.The CNN coal and hardness dynamic perception module is used to analyze the collected cutting motor current,cutting arm vibration acceleration,and rotary oil cylinder pressure data information to perceive the characteristics of coal and;the cutting arm swing speed fuzzy PID control module is used to process the perceived data for fuzzification and defuzzification,and to output the corresponding control parameter signals the electro-hydraulic proportional valve controls the flow and pressure of hydraulic oil according to the received signals,and then the valve-controlled hydraulic cylinder controls the swing speed of cutting arm,achieving the adaptive control of the cutting arm swing speed.The experimental results in the field show that when the roadheader cuts softer media and coal,the arm works at a high swing speed;when cutting complex rock strata,the swing speed decreases as the cutting signal increases,and the cutting signal varies between 0-1;when the roadheader cuts harder rock strata,the cutting load signal is close to 1,and the swing speed of the cutting arm is reduced 0.关键词
悬臂式掘进机/智能截割/截割臂摆速/截割路径/模糊PID控制/煤岩硬度动态感知/卷积神经网络Key words
cantilever roadheader/intelligent cutting/cutting arm swing speed/cutting path/fuzzy PID control/dynamic perception of coal and rock hardness/convolution neural network分类
矿业与冶金引用本文复制引用
李英娜,崔彦平,安博烁,刘百健,靳建伟..基于卷积神经网络和模糊PID的掘进机截割控制系统研究[J].工矿自动化,2025,51(1):61-70,137,11.基金项目
中央引导地方科技发展资金项目(226Z1906G) （226Z1906G）
河北省高等学校自然科学研究项目(CXY2024038) （CXY2024038）
石家庄市驻冀高校基础研究项目(241791157A). （241791157A）
