留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

新型萤火虫算法及在刮板输送机伸缩机尾PID控制中的应用

毛君 郭浩 陈洪月

毛君, 郭浩, 陈洪月. 新型萤火虫算法及在刮板输送机伸缩机尾PID控制中的应用[J]. 机械科学与技术, 2018, 37(9): 1366-1371. doi: 10.13433/j.cnki.1003-8728.20180065
引用本文: 毛君, 郭浩, 陈洪月. 新型萤火虫算法及在刮板输送机伸缩机尾PID控制中的应用[J]. 机械科学与技术, 2018, 37(9): 1366-1371. doi: 10.13433/j.cnki.1003-8728.20180065
Mao Jun, Guo Hao, Chen Hongyue. A New Firefly Algorithm and its Application to PID Control of Scraper Conveyor's Extensible Tail[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(9): 1366-1371. doi: 10.13433/j.cnki.1003-8728.20180065
Citation: Mao Jun, Guo Hao, Chen Hongyue. A New Firefly Algorithm and its Application to PID Control of Scraper Conveyor's Extensible Tail[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(9): 1366-1371. doi: 10.13433/j.cnki.1003-8728.20180065

新型萤火虫算法及在刮板输送机伸缩机尾PID控制中的应用

doi: 10.13433/j.cnki.1003-8728.20180065
基金项目: 

国家自然科学基金项目(51774162)与国家能源研发(实验)中心重大项目(2010_215)资助

详细信息
    作者简介:

    毛君(1960-),教授,博士生导师,研究方向为机械动态设计与仿真,1370418108@139.com

A New Firefly Algorithm and its Application to PID Control of Scraper Conveyor's Extensible Tail

  • 摘要: 为了提高刮板输送机伸缩机尾控制系统的工作性能,提出了一种新型萤火虫算法应用于机尾PID控制器的控制策略。在标准萤火虫算法的动态决策域半径更新公式中,为克服优化初期寻优速度慢和增强算法的全局探测能力,对其中决策域更新系数进行改进;同时利用步长单调递减对位置更新公式中移动步长进行改进,以增强算法后期的深度搜索能力。研究结果表明:新型萤火虫算法的精度及稳定性均优于原算法。建立刮板输送机伸缩机尾电液控制系统数学模型,利用新型萤火虫算法进行PID参数整定优化,并引入能量指标和超调量指标改进适应度函数,优化后的刮板输送机伸缩机尾控制系统具有良好控制品质和鲁棒性。
  • [1] 孟国营,李国平,沃磊,等.重型刮板输送机成套装备智能化关键技术[J].煤炭科学技术,2014,42(9):57-60 Meng G Y, Li G P, Wo L, et al. Intelligent key technologies of complete heavy scraper conveyor equipment[J]. Coal Science and Technology, 2014,42(9):57-60(in Chinese)
    [2] 于林.矿用重型刮板输送机断链故障监测传感器研究[J].煤炭学报,2011,36(11):1934-1937 Yu L. Research on sensor used to detect chain-broken on armoured face conveyor[J]. Journal of China Coal Society, 2011,36(11):1934-1937(in Chinese)
    [3] Krishnanand K N, Ghose D. Detection of multiple source locations using a glowworm metaphor with applications to collective robotic[C]//Proceedings of 2005 IEEE Swarm Intelligence Symposium. Pasadena, CA, USA:IEEE, 2005:84-91
    [4] 王永泉,罗建军.基于多群体改进萤火虫算法的UCAV协同多目标分配[J].西北工业大学学报,2014,32(3):451-456 Wang Y Q, Luo J J. Target assignment in cooperative attacking of UCAVs based on multi-intelligence improved glowworm swarm optimization algorithm[J]. Journal of Northwestern Polytechnical University, 2014,32(3):451-456(in Chinese)
    [5] 杜鹏桢,唐振民,陆建峰,等.不确定环境下基于改进萤火虫算法的地面自主车辆全局路径规划方法[J].电子学报,2014,42(3):616-624 Du P Z, Tang Z M, Lu J F, et al. Global path planning for ALV based on improved glowworm swarm optimization under uncertain environment[J]. Acta Electronica Sinica, 2014,42(3):616-624(in Chinese)
    [6] 张军丽,周永权.一种用Powell方法局部优化的人工萤火虫算法[J].模式识别与人工智能,2011,24(5):680-684 Zhang J L, Zhou Y Q. An artificial glowworm swarm optimization algorithm based on powell local optimization method[J]. Pattern Recognition and Artificial Intelligence, 2011,24(5):680-684(in Chinese)
    [7] Eiben A E, Schippers C A. On evolutionary exploration and exploitation[J]. Fundamenta Informaticae, 1998,35(1-4):35-50
    [8] Črepinšek M, Liu S H, Mernik M. Exploration and exploitation in evolutionary algorithms:a survey[J]. ACM Computing Surveys (CSUR), 2013,45(3):Article No.35
    [9] Emile A, Lenstra J K. Local search in combinatorial optimization[M]. Princeton:Princeton University Press, 2003
    [10] 陈海东,庄平,夏建矿,等.基于改进萤火虫算法的分布式电源优化配置[J].电力系统保护与控制,2016,44(1):149-154 Chen H D, Zhuang P, Xia J K, et al. Optimal power flow of distribution network with distributed generation based on modified firefly algorithm[J]. Power System Protection and Control, 2016,44(1):149-154(in Chinese)
    [11] 陈家星,崔国民,朱玉双,等.改进萤火虫算法应用于换热网络最优化[J].高校化学工程学报,2017,31(1):170-178 Chen J X, Cui G M, Zhu Y S, et al. Optimization of heat exchanger network with improved firefly algorithm[J]. Journal of Chemical Engineering of Chinese Universities, 2017,31(1):170-178(in Chinese)
    [12] 刘景艳,王福忠,李玉东.基于粒子群网络的提升机制动系统故障诊断[J].控制工程,2016,23(2):294-298 Liu J Y, Wang F Z, Li Y D. Fault diagnosis of hoist braking system based on neural network optimized by particle swarm[J]. Control Engineering of China, 2016,23(2):294-298(in Chinese)
    [13] 朱霄珣,徐搏超,焦宏超,等.遗传算法对SVR风速预测模型的多参数优化[J].电机与控制学报,2017,21(2):70-75 Zhu X X, Xu B C, Jiao H C, et al. Windspeed prediction method based on SVR and multi-parameter optimization of GA[J]. Electric Machines and Control, 2017,21(2):70-75(in Chinese)
    [14] 胡坤,张长建,王爽,等.基于改进TLBO算法的刮板输送机伸缩机尾PID控制系统优化[J].中南大学学报(自然科学版),2017,48(1):106-111 Hu K, Zhang C J, Wang S, et al. Optimization of scraper conveyor extensible tail PID control system based on improved TLBO algorithm[J]. Journal of Central South University (Science and Technology), 2017,48(1):106-111(in Chinese)
    [15] 王晶.基于PID控制的带式输送机液压拉紧系统[J].辽宁工程技术大学学报(自然科学版),2010,29(S):102-104 Wang J. Study on hydraulic tensioning system of belt conveyors based on PID control[J]. Journal of Liaoning Technical University (Natural Science), 2010,29(S):102-104(in Chinese)
    [16] 卢彬彬,肖玲斐,龚仁吉,等.基于人工蜂群算法的航空发动机参数自整定PID控制[J].推进技术,2015,36(1):130-135 Lu B B, Xiao L F, Gong R J, et al. Self-Tuning PID control for aeroengine based on artificial bee colony algorithm[J]. Journal of Propulsion Technology, 2015,36(1):130-135(in Chinese)
    [17] 曹青松,洪芸芸,周继惠,等.基于PSO自整定PID控制器的柔性臂振动控制[J].振动、、测试与诊断,2014,34(6):1045-1049 Cao Q S, Hong Y Y, Zhou J H, et al. Vibration control of flexible manipulator based on Self-Tuning PID controller by PSO[J]. Journal of Vibration, Measurement & Diagnosis, 2014,34(6):1045-1049(in Chinese)
    [18] 毛君,张东升,师建国.刮板输送机张力自动控制系统的仿真研究[J].系统仿真学报,2008,20(16):4474-4484 Mao J, Zhang D S, Shi J G. Simulation research of tension automatic control system of scraper conveyor[J]. Journal of System Simulation, 2008,20(16):4474-4484(in Chinese)
    [19] 董立红,赵鹏兵.带式输送机拉紧装置张力的灰色预测PID控制[J].煤炭学报,2013,38(2):342-347 Dong L H, Zhao P B. Grey predictive PID control of the tensioning device system in belt conveyor[J]. Journal of China Coal Society, 2013,38(2):342-347(in Chinese)
    [20] 王文斌.机械设计手册:液压传动与控制[M].北京:机械工业出版社,2007 Wang W B. Mechanical design handbook:pneumatic drive and control[M]. Beijing:China Machine Press, 2007(in Chinese)
  • 加载中
计量
  • 文章访问数:  333
  • HTML全文浏览量:  36
  • PDF下载量:  114
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-10-19
  • 刊出日期:  2018-09-05

目录

    /

    返回文章
    返回