Volume 50 Issue 6
Jun.  2024
Turn off MathJax
Article Contents
Article Navigation >  Journal of Mine Automation  > 2024  >  50(6): 23-29, 45
ZHANG Fan, SHAO Guangyao, LI Yuhan, et al. Adaptive impact resistance support method for advanced hydraulic supports in mines based on digital twins and deep reinforcement learning[J]. Journal of Mine Automation，2024，50（6）：23-29, 45.  doi: 10.13272/j.issn.1671-251x.2023090004
Citation: ZHANG Fan, SHAO Guangyao, LI Yuhan, et al. Adaptive impact resistance support method for advanced hydraulic supports in mines based on digital twins and deep reinforcement learning[J]. Journal of Mine Automation，2024，50（6）：23-29, 45.  doi: 10.13272/j.issn.1671-251x.2023090004
shu

Adaptive impact resistance support method for advanced hydraulic supports in mines based on digital twins and deep reinforcement learning

doi: 10.13272/j.issn.1671-251x.2023090004
  • 1.

    School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China

  • 2.

    Key Laboratory of Intelligent Mining and Robotics of Emergency Management Department, Beijing 100083, China

  • 3.

    Institute of Intelligent Mining and Robotics, China University of Mining and Technology-Beijing, Beijing 100083, China

  • Received Date: 2023-09-01
  • Rev Recd Date: 2024-06-27
    • Available Online: 2024-07-04
    Abstract
  • Due to the disturbance of geological disasters such as deep mining and rock burst, there are problems such as poor self perception capability, weak intelligent anti impact adaptive capability, and lack of decision-making and control capability in the advanced support system of the mine. In order to solve the above problems, a adaptive impact resistance support method for advanced hydraulic supports in mines based on digital twins and deep reinforcement learning is proposed. By sensing the roadway environment and advanced hydraulic support status through multiple sensors, a digital twin model of a physical entity is created in a virtual world. The physical model accurately displays the structural features and details of the advanced hydraulic support. The control model realizes adaptive control of the advanced hydraulic support. The mechanism model realizes logical description and mechanism explanation of the adaptive support of the advanced hydraulic support. The data model stores the physical operation data and twin data of the advanced hydraulic support. The simulation model completes the simulation of the advanced hydraulic support column to achieve virtual real interaction between the advanced hydraulic support and the digital twin model. According to the adaptive impact resistance decision-making algorithm based on deep Q-network (DQN) for advanced hydraulic support, intelligent decision-making is made for roadway impact resistance support in the simulation environment. Based on the decision results, control instructions are issued to physical entities and digital twin models to achieve intelligent control of advanced hydraulic support. The experimental results show that the displacement and pressure changes of the column are consistent, indicating that the simulation model design of the advanced hydraulic support column is reasonable, thereby verifying the accuracy of the digital twin model. The adaptive impact resistance decision-making algorithm for advanced hydraulic supports in mines based on DQN can adjust the PID parameters of the hydraulic support controller, adaptively regulate the column pressure, improve the safety level of roadways, and achieve adaptive impact resistance support for advanced hydraulic supports.

     

    • FullText(HTML)
  • loading
    • References(21)
  • [1]
    吴拥政,付玉凯,何杰,等. 深部冲击地压巷道“卸压−支护−防护”协同防控原理与技术[J]. 煤炭学报,2021,46(1):132-144.

    WU Yongzheng,FU Yukai,HE Jie,et al. Principle and technology of "pressure relief-support-protection" collaborative prevention and control in deep rock burst roadway[J]. Journal of China Coal Society,2021,46(1):132-144.
    [2]
    王国法,牛艳奇. 超前液压支架与围岩耦合支护系统及其适应性研究[J]. 煤炭科学技术,2016,44(9):19-25.

    WANG Guofa,NIU Yanqi. Study on advance hydraulic powered support and surrounding rock coupling support system and suitability[J]. Coal Science and Technology,2016,44(9):19-25.
    [3]
    唐治,潘一山,朱小景,等. 自移式吸能防冲巷道超前支架设计与研究[J]. 煤炭学报,2016,41(4):1032-1037.

    TANG Zhi,PAN Yishan,ZHU Xiaojing,et al. Design and study of self-moving energy absorption and antiimpact roadway advanced support[J]. Journal of China Coal Society,2016,41(4):1032-1037.
    [4]
    潘一山,齐庆新,王爱文,等. 煤矿冲击地压巷道三级支护理论与技术[J]. 煤炭学报,2020,45(5):1585-1594.

    PAN Yishan,QI Qingxin,WANG Aiwen,et al. Theory and technology of three levels support in bump-prone roadway[J]. Journal of China Coal Society,2020,45(5):1585-1594.
    [5]
    杜明超,张坤,黄梁松,等. 新型抗冲击吸能装置手性结构吸能特性对比分析[J/OL]. 煤炭科学技术:1-9[2023-08-17]. https://doi.org/10.13199/j.cnki.cst.2022-1138.

    DU Mingchao,ZHANG Kun,HUANG Liangsong,et al. Comparative analysis of energy absorption characteristics of chiral structures of new impact absorbing devices[J/OL]. Coal Science and Technology:1-9[2023-08-17]. https://doi.org/10.13199/j.cnki.cst.2022-1138.
    [6]
    宿吉奎,廉自生. 乳化液泵站的模糊PID控制系统仿真[J]. 煤矿机械,2012,33(7):54-56. doi: 10.3969/j.issn.1003-0794.2012.07.026

    SU Jikui,LIAN Zisheng. Simulation of fuzzy-PID control system in emulsion pump station[J]. Coal Mine Machinery,2012,33(7):54-56. doi: 10.3969/j.issn.1003-0794.2012.07.026
    [7]
    ESSA M E S M,ABOELELA M A,HASSAN M A M. Position control of hydraulic servo system using proportional-integral-derivative controller tuned by some evolutionary techniques[J]. Journal of Vibration and Control,2016,22(12):2946-2957. doi: 10.1177/1077546314551445
    [8]
    KIM S W,CHO B,SHIN S,et al. Force control of a hydraulic actuator with a neural network inverse model[J]. IEEE Robotics and Automation Letters,2021,6(2):2814-2821. doi: 10.1109/LRA.2021.3062353
    [9]
    FENG Hao,SONG Qianyu,MA Shoulei,et al. A new adaptive sliding mode controller based on the RBF neural network for an electro-hydraulic servo system[J]. ISA Transactions,2022,129:472-484. doi: 10.1016/j.isatra.2021.12.044
    [10]
    LIU Huaiqin,YU Qinghe,WU Qu. PID control model based on back propagation neural network optimized by adversarial learning-based grey wolf optimization[J]. Applied Sciences,2023,13(8). DOI: 10.3390/app13084767.
    [11]
    薛光辉,管健,柴敬轩,等. 基于神经网络PID综掘巷道超前支架支撑力自适应控制[J]. 煤炭学报,2019,44(11):3596-3603.

    XUE Guanghui,GUAN Jian,CHAI Jingxuan,et al. Adaptive control of advance bracket support force in fully mechanized roadway based on neural network PID[J]. Journal of China Coal Society,2019,44(11):3596-3603.
    [12]
    胡相捧,刘新华,庞义辉,等. 基于BP神经网络PID的液压支架初撑力自适应控制[J]. 矿业科学学报,2020,5(6):662-671.

    HU Xiangpeng,LIU Xinhua,PANG Yihui,et al. Adaptive control of setting load of hydraulic support based on BP neural network PID[J]. Journal of Mining Science and Technology,2020,5(6):662-671.
    [13]
    徐亚军,张坤,李丁一,等. 超前支架自适应支护理论与应用[J]. 煤炭学报,2020,45(10):3615-3624.

    XU Yajun,ZHANG Kun,LI Dingyi,et al. Theory and application of self-adaptive support for advanced powered support[J]. Journal of China Coal Society,2020,45(10):3615-3624.
    [14]
    张帆,葛世荣,李闯. 智慧矿山数字孪生技术研究综述[J]. 煤炭科学技术,2020,48(7):168-176.

    ZHANG Fan,GE Shirong,LI Chuang. Research summary on digital twin technology for smart mines[J]. Coal Science and Technology,2020,48(7):168-176.
    [15]
    EL BAZI N,MABROUKI M,LAAYATI O,et al. Generic multi-layered digital-twin-framework-enabled asset lifecycle management for the sustainable mining industry[J]. Sustainability,2023,15(4). DOI: 10.3390/su15043470.
    [16]
    葛世荣,张帆,王世博,等. 数字孪生智采工作面技术架构研究[J]. 煤炭学报,2020,45(6):1925-1936.

    GE Shirong,ZHANG Fan,WANG Shibo,et al. Digital twin for smart coal mining workface:technological frame and construction[J]. Journal of China Coal Society,2020,45(6):1925-1936.
    [17]
    张帆,葛世荣. 矿山数字孪生构建方法与演化机理[J]. 煤炭学报,2023,48(1):510-522.

    ZHANG Fan,GE Shirong. Construction method and evolution mechanism of mine digital twins[J]. Journal of China Coal Society,2023,48(1):510-522.
    [18]
    陈博,朱斌,王蒙飞,等. 基于DDPG的矿井智能通风决策算法研究[J]. 煤矿机械,2023,44(10):201-204.

    CHEN Bo,ZHU Bin,WANG Mengfei,et al. Research on intelligent ventilation decision-making algorithm for mine based on DDPG[J]. Coal Mine Machinery,2023,44(10):201-204.
    [19]
    CAO Jiaqi,WANG Xiaolan,WANG Yansong,et al. An improved dueling deep Q-network with optimizing reward functions for driving decision method[J]. Proceedings of the Institution of Mechanical Engineers,Part D:Journal of Automobile Engineering,2023,237(9):2295-2309. doi: 10.1177/09544070221106037
    [20]
    刘清,张龙,李天越,等. 综采工作面三机数字孪生及协同建模方法[J]. 工矿自动化,2023,49(2):47-55.

    LIU Qing,ZHANG Long,LI Tianyue,et al. A three machine digital twin and collaborative modeling method for fully mechanized working face[J]. Journal of Mine Automation,2023,49(2):47-55.
    [21]
    潘一山,肖永惠,罗浩,等. 冲击地压矿井安全性研究[J]. 煤炭学报,2023,48(5):1846-1860.

    PAN Yishan,XIAO Yonghui,LUO Hao,et al. Study on safety of rockburst mine[J]. Journal of China Coal Society,2023,48(5):1846-1860.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(5)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (476) PDF downloads(67) Cited by()
    Proportional views
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co. Ltd.  Visits：    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return