Research on the roadheader cutting control system based on convolutional neural network and fuzzy PID
李英娜崔彦平安博烁刘百健靳建伟
LI Yingna;CUI Yanping;AN Boshuo;LIU Baijian;JIN Jianwei
石家庄煤矿机械有限责任公司河北科技大学 机械工程学院河北科技大学 材料科学与工程学院
针对悬臂式掘进机在掘进过程中面对煤岩硬度复杂变化时适应性不足、系统稳定性低等问题,提出一种基于卷积神经网络(CNN)及模糊PID的掘进机截割控制系统,该系统包括巷道断面成形特性和智能截割控制策略2个部分,其中掘进机智能截割控制策略由CNN煤岩硬度动态感知模块和截割臂摆速模糊PID控制模块组成。提出一种有效的截割路径,使截割头沿规划路径从上至下进行煤岩截割,以提高断面完整性,减小掘进方向的误差。采用CNN煤岩硬度动态感知模块分析采集的截割电动机电流、截割臂振动加速度、回转油缸压力数据信息,以感知煤岩特性;采用截割臂摆速模糊PID控制模块对感知后的数据进行模糊化与解模糊化处理,输出相应控制参数信号;电液比例阀根据接收到的信号控制液压油的流量和压力,通过阀控液压缸控制截割臂摆速,实现截割臂摆速的自适应控制。现场实验结果表明:当掘进机截割较软介质与煤时,截割臂以高摆速工作;当掘进机截割复杂岩层时,摆速随截割信号的增大而降低,截割信号在0~1之间变动;当掘进机截割较硬岩层时,截割载荷信号接近1,截割臂的摆速降低至0。
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.
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
cantilever roadheader;intelligent cutting;cutting arm swing speed;cutting path;fuzzy PID control;dynamic perception of coal and rock hardness;convolution neural network
主办单位:煤炭科学研究总院有限公司 中国煤炭学会学术期刊工作委员会