Volume 50 Issue 6
Jun.  2024
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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
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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.

     

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