Volume 49 Issue 7
Jul.  2023
Turn off MathJax
Article Contents
Abstract
References
Related Articles
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

  • 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

More Information
  • Received Date: October 09, 2022
  • Revised Date: July 15, 2023
  • Available Online: August 02, 2023
    Graphical Abstract
    • 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.
    • FullText(HTML)
    • References (21)
  • [1]
    秦海忠,付玉凯,王涛. 深部复合顶板巷道变形破坏特征及支护技术[J]. 工矿自动化,2020,46(10):80-86. DOI: 10.13272/j.issn.1671-251x.2020020009

    QIN Haizhong,FU Yukai,WANG Tao. Deformation and failure characteristics and support technology of deep roadway with composite roof[J]. Industry and Mine Automation,2020,46(10):80-86. DOI: 10.13272/j.issn.1671-251x.2020020009
    [2]
    朱俊福. 深部层状岩体巷道围岩松动圈形成机理及其工程应用研究[D]. 徐州: 中国矿业大学, 2021.

    ZHU Junfu. Study on the formation mechanism and its engineering application of broken rock zone in deep bedded rock mass[D]. Xuzhou: China University of Mining and Technology, 2021.
    [3]
    张铁军,李伟涛,尹松阳. 深部开采巷道掘进工作面受力特征及合理空顶距分析[J]. 煤炭科技,2022,43(5):50-53,57.

    ZHANG Tiejun,LI Weitao,YIN Songyang. Analysis of the stress characteristics and reasonable space between roadway and roof in deep mining[J]. Coal Science & Technology Magazine,2022,43(5):50-53,57.
    [4]
    郭文孝. 交叉迈步式快速掘进临时支护支架组的研究[J]. 煤矿机械,2014,35(12):187-189. DOI: 10.13436/j.mkjx.201412079

    GUO Wenxiao. Research on rapid excavation and temporary support of moving cross-type support group[J]. Coal Mine Machinery,2014,35(12):187-189. DOI: 10.13436/j.mkjx.201412079
    [5]
    薛光辉,管健,程继杰,等. 深部综掘巷道超前支架设计与支护性能分析[J]. 煤炭科学技术,2018,46(12):15-20. DOI: 10.13199/j.cnki.cst.2018.12.003

    XUE Guanghui,GUAN Jian,CHENG Jijie,et al. Design of advance support for deep fully-mechanized heading roadway and its support performance analysis[J]. Coal Science and Technology,2018,46(12):15-20. DOI: 10.13199/j.cnki.cst.2018.12.003
    [6]
    卢进南. 综掘巷道迈步式超前支护系统力学特性研究[D]. 阜新: 辽宁工程技术大学, 2014.

    LU Jinnan. Study on mechanical properties of stepping advance support system in fully mechanized roadway [D]. Fuxin: Liaoning University of Engineering and Technology, 2014.
    [7]
    王国法,庞义辉,李明忠,等. 超大采高工作面液压支架与围岩耦合作用关系[J]. 煤炭学报,2017,42(2):518-526. DOI: 10.13225/j.cnki.jccs.2016.0699

    WANG Guofa,PANG Yihui,LI Mingzhong,et al. Hydraulic support and coal wall coupling relationship in ultra large height mining face[J]. Journal of China Coal Society,2017,42(2):518-526. DOI: 10.13225/j.cnki.jccs.2016.0699
    [8]
    栾丽君,赵慧萌,谢苗,等. 超前支架速度、压力稳定切换控制策略研究[J]. 机械强度,2017,39(4):747-753. DOI: 10.16579/j.issn.1001.9669.2017.04.001

    LUAN Lijun,ZHAO Huimeng,XIE Miao,et al. Research on speed and pressure control strategy of stable switch about forepoling equipment[J]. Journal of Mechanical Strength,2017,39(4):747-753. DOI: 10.16579/j.issn.1001.9669.2017.04.001
    [9]
    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. DOI: 10.19606/j.cnki.jmst.2020.06.009

    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. DOI: 10.19606/j.cnki.jmst.2020.06.009
    [10]
    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. DOI: 10.13225/j.cnki.jccs.2018.1688

    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. DOI: 10.13225/j.cnki.jccs.2018.1688
    [11]
    姜磊,叶圣超,李飞龙. 基于PSO−BP神经网络的采煤机电动机故障诊断研究[J]. 矿山机械,2020,48(9):59-64. DOI: 10.16816/j.cnki.ksjx.2020.09.012

    JIANG Lei,YE Shengchao,LI Feilong. Research on fault diagnosis of shearer motor based on PSO-BP neural network[J]. Mining & Processing Equipment,2020,48(9):59-64. DOI: 10.16816/j.cnki.ksjx.2020.09.012
    [12]
    陈兰. 液压支架液压系统的建模与仿真[D]. 西安: 西安科技大学, 2011.

    CHEN Lan. Modeling and simulation of hydraulic support hydraulic system[D]. Xi'an: Xi'an University of Science and Technology, 2011.
    [13]
    冯玉芳,卢厚清,殷宏,等. 基于BP神经网络的故障诊断模型研究[J]. 计算机工程与应用,2019,55(6):24-30.

    FENG Yufang,LU Houqing,YIN Hong,et al. Study on fault diagnosis model based on BP neural network[J]. Computer Engineering and Applications,2019,55(6):24-30.
    [14]
    邵建浩,张婷. 基于BP神经网络的SCARA机器人故障诊断[J]. 机床与液压,2022,50(14):166-170. DOI: 10.3969/j.issn.1001-3881.2022.14.030

    SHAO Jianhao,ZHANG Ting. Fault diagnosis of SCARA robot based on BP neural network[J]. Machine Tool & Hydraulics,2022,50(14):166-170. DOI: 10.3969/j.issn.1001-3881.2022.14.030
    [15]
    袁建平,施一萍,蒋宇,等. 改进的BP神经网络PID控制器在温室环境控制中的研究[J]. 电子测量技术,2019,42(4):19-24. DOI: 10.19651/j.cnki.emt.1802034

    YUAN Jianping,SHI Yiping,JIANG Yu,et al. Research on improved BP neural network PID controller in greenhouse environment control[J]. Electronic Measurement Technology,2019,42(4):19-24. DOI: 10.19651/j.cnki.emt.1802034
    [16]
    谢宇希,颜拥军,李翔,等. 基于BP神经网络的核探测器故障诊断方法研究[J]. 原子能科学技术,2021,55(10):1857-1864. DOI: 10.7538/yzk.2020.youxian.0716

    XIE Yuxi,YAN Yongjun,LI Xiang,et al. Study of nuclear detector fault diagnosis method based on BP neural network[J]. Atomic Energy Science and Technology,2021,55(10):1857-1864. DOI: 10.7538/yzk.2020.youxian.0716
    [17]
    XU Xianzhen, CAO Dan, ZHOU Yu, et al. Application of neural network algorithm in fault diagnosis of mechanical intelligence[J]. Mechanical Systems and Signal Processing, 2020, 141. DOI: 10.1016/j.ymssp.2020.106625.
    [18]
    WU Yanmin, SONG Qipeng. Improved particle swarm optimization algorithm in power system network reconfiguration[J]. Mathematical Problems in Engineering, 2021, 2021. DOI: 10.1155/2021/5574501.
    [19]
    田劼,银晓琦,文艺成. 基于混合IWO−PSO算法的掘进机截割轨迹规划方法[J]. 工矿自动化,2021,47(12):55-61.

    TIAN Jie,YIN Xiaoqi,WEN Yicheng. Method of cutting trajectory planning of roadheader based on hybrid IWO-PSO algorithm[J]. Industry and Mine Automation,2021,47(12):55-61.
    [20]
    施昕昕,费军. 基于PSO−BP的直线电机轨迹跟踪自抗扰控制器设计[J]. 组合机床与自动化加工技术,2023(6):132-135. DOI: 10.13462/j.cnki.mmtamt.2023.06.030

    SHI Xinxin,FEI Jun. Design of active disturbance rejection controller for linear motor trajectory tracking based on PSO-BP[J]. Modular Machine Tool & Automatic Manufacturing Technique,2023(6):132-135. DOI: 10.13462/j.cnki.mmtamt.2023.06.030
    [21]
    KAHOULI O, ALSAIF H, BOUTERAA Y, et al. Power system reconfiguration in distribution network for improving reliability using genetic algorithm and particle swarm optimization[J]. Applied Sciences, 2021, 11（7）. DOI: 10.3390/app11073092.
    • Related Articles

  • Related Articles

    [1]MA Changqing, LI Feng, HUANG Yubo, MAO Junjie, LI Xuyang, WEI Xiangyu, MA Xiaoyang. Research on adaptive control of self-moving temporary support based on fuzzy PID[J]. Journal of Mine Automation, 2024, 50(12): 76-84. DOI: 10.13272/j.issn.1671-251x.2024070064
    [2]CUI Lizhen, XU Fanfei, WANG Qiaoli, GAO Lili. Underground adaptive positioning algorithm based on PSO-BP neural network[J]. Journal of Mine Automation, 2018, 44(2): 74-79. DOI: 10.13272/j.issn.1671-251x.2017090028
    [3]GUO Xijin, SHAO Hongqing, YANG Chunbao, ZHANG Zhiqiang. Research on PFC-PID control algorithm of density and liquid level in heavy medium suspensio[J]. Journal of Mine Automation, 2018, 44(1): 89-95. DOI: 10.13272/j.issn.1671-251x.2017030088
    [4]SUN Xiao-xi, HUANG You-rui, QU Li-guo. Research of time-delay control of wireless network based on RBF neural network PID control[J]. Journal of Mine Automation, 2013, 39(12): 76-81. DOI: 10.7526/j.issn.1671-251X.2013.12.019
    [5]GUO Xing-ge, WU Jiao-jiao, LIU Jing, SUN Li. Self-tuning fault-tolerant PID control for mine hoist based on BP neural network[J]. Journal of Mine Automation, 2013, 39(6): 45-48.
    [6]YU Qiong-xia, WANG Fu-zhong. Design of Speed-regulation System of PMLSM Based on BP Neural Network PID Control[J]. Journal of Mine Automation, 2011, 37(5): 34-37.
    [7]WANG Jun-xiao, LIN Qing-song, YAO Yu-fei, LIU Yan-bing. Application Research of PID Controller Based on BP Neural Network in Servo System[J]. Journal of Mine Automation, 2010, 36(5): 68-70.
    [8]FU Hua, LI Da-zhi. PID Self-tuning Control System Based on Neural Network[J]. Journal of Mine Automation, 2009, 35(7): 72-75.
    [9]LIU Shi-xian, ZHU Hua, WANG Yong, ZHU Feng-pei. Design of Control System of Emulsion Liquid Level Based on Fuzzy-PID Control[J]. Journal of Mine Automation, 2009, 35(4): 17-19.
    [10]LI Zu-xin , ZHANG Yu-feng , SHI Xin-ling . A New P-FUZZY-PID Controller with Switching Based on Fuzzy Rules[J]. Journal of Mine Automation, 2003, 29(1): 4-6.

  • Cited by

    Periodical cited type(7)

    1. 汤捷中. 基于遗传算法优化BP神经网络的内燃机轴承故障诊断方法. 内燃机与配件. 2024(03): 19-21 .
    2. 张能，汪川迪，安鹏，张羽，王利达，王杰. 基于PSO-BP的油气井钻速预测技术优化与仿真实验. 粘接. 2024(06): 19-22 .
    3. 赵庆浩，周军，荆丰伟. 基于自适应变异的粒子群优化BP神经网络的液压缸故障诊断方法. 机电产品开发与创新. 2024(04): 103-107 .
    4. 马宏伟，李烺，薛旭升，王川伟，王赛赛，赵英杰，周文剑，张恒. 护盾式临时支护机器人带压行驶液压控制系统研究. 工矿自动化. 2024(07): 21-31 . 本站查看
    5. 姚钰鹏，熊武. 基于动态自适应旗鱼优化BP神经网络的工作面周期来压预测. 工矿自动化. 2024(08): 30-37 . 本站查看
    6. 黄登臣. 基于粒子群算法的临时支架优化分析. 西部交通科技. 2024(08): 84-87 .
    7. 王云飞，赵继云，张鹤，王浩，张阳. 基于神经网络补偿的液压支架群推移系统直线度控制方法. 煤炭科学技术. 2024(11): 174-185 .

    Other cited types(1)

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (222) PDF downloads (29) Cited by(8)
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co., Ltd. Visits：1171946    Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return