Volume 49 Issue 7
Jul.  2023
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Article Navigation >  Journal of Mine Automation  > 2023  >  49(7): 67-74
TIAN Jie, LI Yang, ZHANG Lei, et al. Adaptive control of temporary support force based on PSO-BP neural network[J]. Journal of Mine Automation，2023，49(7)：67-74.  doi: 10.13272/j.issn.1671-251x.2022100017
Citation: TIAN Jie, LI Yang, ZHANG Lei, et al. Adaptive control of temporary support force based on PSO-BP neural network[J]. Journal of Mine Automation，2023，49(7)：67-74.  doi: 10.13272/j.issn.1671-251x.2022100017
shu

Adaptive control of temporary support force based on PSO-BP neural network

doi: 10.13272/j.issn.1671-251x.2022100017
  • 1.

    School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

  • 2.

    Key Laboratory of Coal Mine Intelligence and Robot Innovative Application, Ministry of Emergency Management, China University of Mining and Technology-Beijing, Beijing 100083, China

  • Received Date: 2022-10-10
  • Rev Recd Date: 2023-07-16
    • Available Online: 2023-08-03
    Abstract
  • In order to make the temporary support force better adapt to the mine pressure and improve the support capacity of the support, taking the dual self-moving temporary support as the research object, an adaptive control method of temporary support force based on particle swarm optimization (PSO) - BP neural network is proposed. The initial weights of the BP neural network are optimized by using the global search capability and fast convergence features of the PSO algorithm to improve the rate of convergence of the BP neural network. Then, the optimized BP neural network is used to achieve online self-adjustment of PID parameters. The PSO-BP neural network is constructed to optimize the PID controller. This enables the temporary support force to reach the predetermined value more quickly and accurately, achieving adaptive control of the temporary support force. It avoids damage to the roof due to the mismatch between support force and roof pressure. The expected initial support force of the temporary support is simulated using unit step signals for experimental verification. The results show that compared with the BP neural network optimized PID controller and traditional PID controller, the PSO-BP neural network optimized PID controller can achieve the expected initial support force faster and more accurately. The adjustment time is only 0.5 s and there is almost no overshoot. Based on actual geological conditions, the roof pressure on the support during excavation support is simulated. The adaptive control effect of three controllers for support force is studied. The results show that under the control of the PSO-BP neural network optimized PID controller, the system error is only 0.02 MPa, with the smallest error and the best control effect.

     

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