Research on the roadheader cutting control system based on convolutional neural network and fuzzy PID
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摘要:
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。
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.
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图 1 基于CNN和模糊PID的掘进机截割控制系统结构
Figure 1. Structure of roadheader cutting control system based on convolutional neural network(CNN) and fuzzy proportional integral derivative(PID)
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图 8 CNN煤岩硬度动态感知模块结构
Figure 8. Structure of the CNN-based coal rock hardness dynamic perception module
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图 9 CNN煤岩硬度动态感知模块的迭代过程
Figure 9. Iterative process of the CNN-based coal rock hardness dynamic perception module
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图 10 截割臂摆速模糊PID控制模块结构
Figure 10. Structure of fuzzy PID control module for cutting arm swing speed
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图 12 截割臂摆速模糊PID控制模块输出
Figure 12. The output of fuzzy PID control module for cutting arm swing speed
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图 13 截割臂摆速仿真控制系统控制流程
Figure 13. Control flow of the cutting arm swing speed simulation control system
表 1 掘进机参数
Table 1 Roadheader parameters
回转关节摆角θ1/（°） 升降关节摆角θ2/（°） 伸缩量d3/mm （−39，39） （−121，−48） [0，500] 
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表 2 样本数据与载荷关系
Table 2 Relationship between sample data and load
参数 不同煤岩种类的参数值 较软介质与煤 煤岩夹杂 硬岩 煤岩硬度 0～3 3～6 ≥6 截割电动机电流/A <68 68～121 ≥121 回转油缸压力/MPa <10 10～21 ≥21 截割臂振动加速度/（m·s−2） <25.40 25.40～56.84 ≥56.84 截割载荷/kN ≤20.29 20.29～54.7 ≥54.7
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表 3 $ {\Delta K}_{{\mathrm{p}}} $模糊规则
Table 3 $ {\Delta K}_{{\mathrm{p}}} $ fuzzy rule
e n NB NM NS ZO PS PM PB NB NB PB PM PM PS ZO ZO NM PB PB PM PS PS ZO NS NS PM PM PM PS ZO NS NS ZO PM PM PS ZO NS NM NM PS PS PS ZO NS NS NM NM PM PS PS NS NM NM NM NB PB ZO ZO NM NM NM NB NB
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表 4 $ \Delta {K}_{{\mathrm{i}}} $模糊规则
Table 4 $ \Delta {K}_{{\mathrm{i}}} $ fuzzy rule
e n NB NM NS ZO PS PM PB NB NB NB NM NM NS ZO ZO NM NB NB NM NS NS ZO ZO NS NB NM NS NS ZO PS PS ZO NM NM NS ZO PS PM PM PS NM NS ZO PS PS PM PB PM ZO ZO PS PS PM PB PB PB ZO ZO PS PM PM PB PB
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表 5 $ \Delta {K}_{{\mathrm{d}}} $模糊规则
Table 5 $ \Delta {K}_{{\mathrm{d}}} $ fuzzy rule
e n NB NM NS ZO PS PM PB NB PS NS NB NB NB NM PS NM PS NS NB NM NM NS ZO NS ZO NS NM NM NS NS ZO ZO ZO NS NS NS NS NS ZO PS ZO ZO ZO ZO ZO ZO ZO PM PB NS PS PS PS PS PB PB PB PM PM PM PS PS PB
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表 6 截割臂摆速仿真控制系统参数
Table 6 Parameters of cutting arm swing speed simulation control system
参数 值 参数 值 $ C_{\mathrm{\alpha\mathrm{ }}} $/$ {({\mathrm{m}}}^{3}·{{\mathrm{s}}^{-1}· {\mathrm{A}}}^{-1}) $ $ 3.5\times {10}^{-4} $ $ {\delta }_{{\mathrm{h}}} $ 0.180 $ {\omega }_{{\mathrm{v}}} $/$ ({\mathrm{rad}}·{\mathrm{s}}^{-1}) $ 157.100 $ {\omega }_{{\mathrm{h}}} $/$ ({\mathrm{rad}}·{\mathrm{s}}^{-1}) $ 55.570 $ {\delta }_{{\mathrm{v}}} $ 0.600 $ {C}_{{\mathrm{b}}} $/$ ((^\circ) ·{{\mathrm{m}}}^{-1}) $ 80 $ {A}_{{\mathrm{P}}} $/$ {m}^{2} $ 0.025 $ {C}_{\alpha } $/$ ({\mathrm{A}}·{{\mathrm{V}}}^{-1}) $ 0.200
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