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综采工作面大流量智能供液系统研究

司明 邬伯藩 王子谦

司明,邬伯藩,王子谦. 综采工作面大流量智能供液系统研究[J]. 工矿自动化,2022,48(7):66-72.  doi: 10.13272/j.issn.1671-251x.2022030033
引用本文: 司明,邬伯藩,王子谦. 综采工作面大流量智能供液系统研究[J]. 工矿自动化,2022,48(7):66-72.  doi: 10.13272/j.issn.1671-251x.2022030033
SI Ming, WU Bofan, WANG Ziqian. Research on large flow intelligent liquid supply system in fully mechanized working face[J]. Journal of Mine Automation,2022,48(7):66-72.  doi: 10.13272/j.issn.1671-251x.2022030033
Citation: SI Ming, WU Bofan, WANG Ziqian. Research on large flow intelligent liquid supply system in fully mechanized working face[J]. Journal of Mine Automation,2022,48(7):66-72.  doi: 10.13272/j.issn.1671-251x.2022030033

综采工作面大流量智能供液系统研究

doi: 10.13272/j.issn.1671-251x.2022030033
基金项目: 国家自然科学基金资助项目(U1261114);陕西省自然科学基础研究计划项目(2019JM-162)。
详细信息
    作者简介:

    司明(1984—),男,宁夏中卫人,高级工程师,硕士,硕士研究生导师,主要研究方向为智能信息处理技术,E-mail:176228107@qq.com

    通讯作者:

    邬伯藩(1997—),男,内蒙古包头人,硕士研究生,主要研究方向为数据挖掘技术, E-mail:1183249012@qq.com

  • 中图分类号: TD355

Research on large flow intelligent liquid supply system in fully mechanized working face

  • 摘要: 针对综采工作面供液系统供液能力不足、压力波动大、系统运行稳定性差等问题,提出了一种免疫粒子群优化模糊神经网络PID(IPSO−FNN−PID)算法,设计了IPSO−FNN−PID控制器,实现了供液系统稳压控制。IPSO−FNN−PID算法将粒子群(PSO)算法和免疫算法(IA)引入模糊神经网络(FNN)PID控制器,针对FNN算法易陷入局部寻优问题,采用免疫粒子群(IPSO)算法优化FNN算法,通过在PSO算法中加入IA来提高PSO算法的收敛性,实现最优PID参数输出。为验证IPSO−FNN−PID控制器的有效性,选取传统PID控制器、Fuzzy−PID控制器、FNN−PID控制器进行比较,仿真结果表明:① IPSO−FNN−PID控制器对乳化液泵的控制效果最佳,其他3种控制器的上升时间、峰值时间和调节时间均比IPSO−FNN−PID控制器长,最大超调量均大于IPSO−FNN−PID控制器。② 在加入扰动信号后,IPSO−FNN−PID控制器具有较好的自适应性和鲁棒性,恢复到平稳状态仅用了1.2 s。③ 当利用传统PID和Fuzzy−PID控制器对乳化液泵进行控制时,振荡明显,超调量大,分别为41.2%,22.3%;当利用FNN−PID控制器对乳化液泵进行控制时,振荡明显减弱,超调量降低为17.6%,调节时间减少至2.68 s;当利用IPSO−FNN−PID控制器对乳化液泵进行控制时,几乎无振荡,超调量仅为5.22%,调节时间缩短至2.61 s,遇到干扰信号时稳定性更强。④ 在受到扰动信号时,负载干扰对IPSO−FNN−PID控制器的影响较小,且收敛迅速,鲁棒性大大提升,表明IPSO−FNN−PID控制器具备良好的抗扰动及扰动补偿能力,可满足供液系统的稳压控制要求。

     

  • 图  1  供液系统结构

    Figure  1.  Liquid supply system structure

    图  2  IPSO−FNN−PID控制器

    Figure  2.  IPSO-FNN-PID controller

    图  3  输入变量和输出变量的隶属度函数曲线

    Figure  3.  Membership function curves of input and output variable

    图  4  IPSO算法粒子寻优位置

    Figure  4.  IPSO algorithm particle optimization position

    图  5  各控制器阶跃响应曲线

    Figure  5.  Step response curve of each controller

    图  6  各控制器扰动仿真结果

    Figure  6.  Disturbance simulation results of each controller

    表  1  模糊控制规则

    Table  1.   Fuzzy control rules

    EC E
    NBNMNSZOPSPMPB
    NBPBPBPMPMPSPSZO
    NMPBPMPMPSPSZONS
    NSPMPMPSPSZONSNS
    ZOPMPSPSZONSNSNM
    PSPSPSZONSNSNMNM
    PMPSZONSNSNMNMNB
    PBZONSNSNMNMNBNB
    下载: 导出CSV

    表  2  各控制器PID控制参数及动态特性比较

    Table  2.   Comparison of PID control parameters and dynamic characteristics of each controller

    控制器${K}_{{\rm{p}}}$${K}_{{\rm{i}}}$${K}_{{\rm{d}}}$$ \mathrm{\sigma } $/%${t}_{{\rm{r}}}$/s${t}_{{\rm{p}}}$/s${t}_{{\rm{s}}}$/s
    PID3.2154.5834.11241.21.011.713.72
    Fuzzy−PID0.6912.8923.67222.31.341.633.56
    FNN−PID0.8823.0003.12717.60.981.552.68
    IPSO−FNN−PID1.2182.6143.7455.220.891.312.61
    下载: 导出CSV
  • [1] 付翔,王然风. 基于意识−情感−智能三位一体的煤矿供液过程控制[J]. 智能系统学报,2018,13(4):640-649.

    FU Xiang,WANG Ranfeng. Hydraulic fluid supply process control of coal mine based on consciousness, emotion,and intelligence[J]. CAAI Transactions on Intelligent Systems,2018,13(4):640-649.
    [2] 王国法. 煤矿智能化最新技术进展与问题探讨[J]. 煤炭科学技术,2022,50(1):1-27.

    WANG Guofa. New technological progress of coal mine intelligence and its problems[J]. Coal Science and Technology,2022,50(1):1-27.
    [3] 李然. 大采高工作面高压大流量乳化液泵的研制及应用[J]. 煤炭科学技术,2017,45(12):145-149.

    LI Ran. Research and development as well as application of high pressure and high flow emulsion pump to large mining height face[J]. Coal Science and Technology,2017,45(12):145-149.
    [4] 李然,王伟. 综采集成供液系统智能监测诊断技术现状与发展[J]. 煤炭科学技术,2016,44(3):91-95.

    LI Ran,WANG Wei. Status and development of intelligent monitoring and diagnosis technology for fully mechanized integrated pressure pumping system[J]. Coal Science and Technology,2016,44(3):91-95.
    [5] 罗文,杨俊彩. 神东矿区薄煤层安全高效开采技术研究[J]. 煤炭科学技术,2020,48(3):68-74.

    LUO Wen,YANG Juncai. Study on safety and efficient mining technology of thin coal seam in Shendong minging area[J]. Coal Science and Technology,2020,48(3):68-74.
    [6] 李然. 综采工作面智能供液技术及发展趋势[J]. 煤炭科学技术,2019,47(9):203-207.

    LI Ran. Intelligent fluid supply technology in fully-mechanized coal mining face and its development trend[J]. Coal Science and Technology,2019,47(9):203-207.
    [7] 李永明. 采煤工作面集中配液及远程供液系统应用[J]. 煤炭科学技术,2021,49(增刊1):183-187.

    LI Yongming. Application of centralized liquid distribution and remote liquid supply system in coal mining face[J]. Coal science and technology,2021,49(S1):183-187.
    [8] 张占东,张榕慧,姚利花,等. 面向液压支架动作过程的乳化液泵站多泵并联供液技术研究[J]. 煤矿机械,2019,40(10):35-37.

    ZHANG Zhandong,ZHANG Ronghui,YAO Lihua,et al. Study on multi-parallel pumps fluid feeding technology of emulsion pump station oriented to hydraulic support movement process[J]. Coal Mine Machine,2019,40(10):35-37.
    [9] 杨国来,朱礼浩,张晓丽,等. 乳化液泵的理论分析与数值模拟[J]. 液压与气动,2015(5):86-88,108. doi: 10.11832/j.issn.1000-4858.2015.05.019

    YANG Guolai,ZHU Llihao,ZHANG Xiaoli,et al. Theoretical analysis and numerical simulation of emulsion pump[J]. Chinese Hydraulics & Pneumatics,2015(5):86-88,108. doi: 10.11832/j.issn.1000-4858.2015.05.019
    [10] 石建华,刘漫贤,张兆杰,等. 矿用乳化液泵站智能化改造与应用[J]. 煤矿机械,2021,42(2):139-141. doi: 10.13436/j.mkjx.202102044

    SHI Jianhua,LIU Manxian,ZHANG Zhaojie,et al. Intelligent transformation and application of mine emulsion pump station[J]. Coal Mine Machine,2021,42(2):139-141. doi: 10.13436/j.mkjx.202102044
    [11] 陈伟,王存飞,边燕. 超大采高综采工作面乳化液泵站系统[J]. 工矿自动化,2021,47(4):6-12. doi: 10.13272/j.issn.1671-251x.2020120020

    CHEN Wei,WANG Cunfei,BIAN Yan. Emulsion pump station system for super high fully mechanized working face[J]. Industry and Mine Automation,2021,47(4):6-12. doi: 10.13272/j.issn.1671-251x.2020120020
    [12] 李文华,刘娇,柴博. 节能液压泵模糊PID控制系统研究与仿真[J]. 控制工程,2017,24(7):1347-1351. doi: 10.14107/j.cnki.kzgc.150500

    LI Wenhua,LIU Jiao,CHAI Bo. Research and simulation of energy efficient fuzzy PID control system for hydraulic pump stations[J]. Control Engineering of China,2017,24(7):1347-1351. doi: 10.14107/j.cnki.kzgc.150500
    [13] 王建国,周燕飞. 泵控马达系统的模糊PID控制与仿真[J]. 机械设计与制造工程,2015,44(11):44-47. doi: 10.3969/j.issn.2095-509X.2015.11.010

    WANG Jianguo,ZHOU Yanfei. Fuzzy PID control and simulation of pump - contro-motor system[J]. Mechanical Design and Manufacturing Engineering,2015,44(11):44-47. doi: 10.3969/j.issn.2095-509X.2015.11.010
    [14] 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 and Science,2020,5(6):662-671. doi: 10.19606/j.cnki.jmst.2020.06.009
    [15] 李浩楠,刘勇. 模糊神经网络的优化及其应用[J]. 哈尔滨理工大学学报,2020,25(6):142-149. doi: 10.15938/j.jhust.2020.06.021

    LI Haonan,LIU Yong. Optimization and application of fuzzy neural network[J]. Journal of Harbin University of Technology,2020,25(6):142-149. doi: 10.15938/j.jhust.2020.06.021
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出版历程
  • 收稿日期:  2022-03-08
  • 修回日期:  2022-06-25
  • 网络出版日期:  2022-05-10

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