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基于改进BP−PID的带式输送机速度控制方法

桂改花 苑占江

桂改花,苑占江. 基于改进BP−PID的带式输送机速度控制方法[J]. 工矿自动化,2023,49(5):104-111.  doi: 10.13272/j.issn.1671-251x.2022080058
引用本文: 桂改花,苑占江. 基于改进BP−PID的带式输送机速度控制方法[J]. 工矿自动化,2023,49(5):104-111.  doi: 10.13272/j.issn.1671-251x.2022080058
GUI Gaihua, YUAN Zhanjiang. Speed control method for belt conveyor based on improved BP-PID[J]. Journal of Mine Automation,2023,49(5):104-111.  doi: 10.13272/j.issn.1671-251x.2022080058
Citation: GUI Gaihua, YUAN Zhanjiang. Speed control method for belt conveyor based on improved BP-PID[J]. Journal of Mine Automation,2023,49(5):104-111.  doi: 10.13272/j.issn.1671-251x.2022080058

基于改进BP−PID的带式输送机速度控制方法

doi: 10.13272/j.issn.1671-251x.2022080058
基金项目: 广东省普通高校特色创新项目(K01057037)。
详细信息
    作者简介:

    桂改花(1981—),女,山东聊城人,讲师,硕士,主要研究方向为数学建模、控制算法,E-mail:ggaihua2022@163.com

  • 中图分类号: TD528.1

Speed control method for belt conveyor based on improved BP-PID

  • 摘要: 针对传统BP−PID控制算法采用梯度下降法求解,存在收敛速度慢、易陷入局部极值且在低信噪比(LSNR)条件下性能下降等问题,提出了一种基于改进遗传模拟退火算法(ImGSAA)优化的BP−PID带式输送机速度控制方法(ImGSAA−BP−PID)。首先将交叉、变异概率取值与迭代时间关联,并引入反余弦函数增加遗传模拟退火算法(GSAA)动态调整和非线性变化适应能力。然后通过对传统Metropolis准则进行加权处理,提出加权Metropolis准则,对新种群个体进行修正,提升GSAA的噪声稳健性。最后利用ImGSAA对BP−PID初始参数进行优化,自动确定BP−PID的最优参数组合,从而提升参数整定的实时性和控制精度及对LSNR环境的适应能力。试验结果表明:① ImGSAA仅需11次迭代即可收敛,表明利用改进的交叉、变异策略和加权Metropolis准则对GSAA进行优化,能够有效提升算法的收敛速度和实时性。② ImGSAA−BP−PID的控制误差为−0.468 5~0.572 3 m/s,与遗传算法(GA)−BP−PID、粒子群算法(PSO)−BP−PID、GSAA−BP−PID的控制方法相比,分别提升了224.88%,104.07%,38.33%。③ ImGSAA性能受LSNR影响最小,迭代15次即收敛于全局最优解,具有较强的噪声稳健性。④ 在LSNR条件下,ImGSAA−BP−PID的控制误差均值下降了3.54%,控制性能明显优于GA−BP−PID,PSO−BP−PID,GSAA−BP−PID,更满足实际工程应用需求。

     

  • 图  1  BP神经网络结构

    Figure  1.  Structure of BP neural network

    图  2  ImGSAA−BP−PID控制流程

    Figure  2.  Control process of the ImGSAA-BP-PID

    图  3  仿真架构

    Figure  3.  Simulation structure

    图  4  各算法迭代过程中参数变化曲线

    Figure  4.  Parameter change curve during iteration of different algorithms

    图  5  4种方法速度控制结果

    Figure  5.  Speed control results of four methods

    图  6  LSNR条件下速度变化曲线

    Figure  6.  Speed change curve under LSNR

    图  7  LSNR条件下参数变化曲线

    Figure  7.  Parameter change curve under LSNR

    图  8  LSNR条件下4种方法的速度控制误差

    Figure  8.  Speed control error of four methods under LSNR

    图  9  LSNR条件下4种方法的控制性能

    Figure  9.  Control performance of four methods under LSNR

    表  1  4种方法控制性能指标

    Table  1.   Control performance indexes of four methods

    控制方法调整
    时间/s
    峰值/V峰值
    时间/s
    最大超
    调量
    误差均值/
    (m·s−1)
    误差均
    方根
    GA−BP−PID0.00561.13820.03124.560.33561.53
    PSO−BP−PID0.00331.08450.02715.310.21080.76
    GSAA−BP−PID0.00201.00560.00710.090.14290.42
    ImGSAA−BP−PID0.00161.00730.0067.110.10330.25
    下载: 导出CSV

    表  2  LSNR条件下4种方法控制性能指标

    Table  2.   Control performance indexes of four methods under LSNR

    调速方法调整
    时间/s
    峰值/V峰值
    时间/s
    最大超
    调量
    误差均值/
    (m·s−1)
    误差均
    方根
    GA−BP−PID0.00791.65780.06234.770.55902.23
    PSO−BP−PID0.00391.10670.03116.520.29100.92
    GSAA−BP−PID0.00551.31820.04723.210.47551.39
    ImGSAA−BP−PID0.00191.03480.0108.210.13870.45
    下载: 导出CSV
  • [1] 方崇全. 煤矿带式输送机巡检机器人关键技术研究[J]. 煤炭科学技术,2022,50(5):263-270.

    FANG Chongquan. Research on key technology of inspection robot for coal mine belt conveyor[J]. Coal Science and Technology,2022,50(5):263-270.
    [2] 冯俊宾. 变频调速技术在带式输送机上的节能应用[J]. 机械研究与应用,2021,34(1):142-144.

    FENG Junbin. Energy saving application of frequency control technology in belt conveyor[J]. Mechanical Research & Application,2021,34(1):142-144.
    [3] 杨春雨,顾振,张鑫,等. 基于深度学习的带式输送机煤流量双目视觉测量[J]. 仪器仪表学报,2021,41(8):164-174.

    YANG Chunyu,GU Zhen,ZHANG Xin,et al. Binocular vision measurement of coal flow of belt conveyors based on deep learning[J]. Chinese Journal of Scientific Instrument,2021,41(8):164-174.
    [4] 胡宗镇,赵延立. 基于改进型BP神经网络自整定的PID控制[J]. 电脑与信息技术,2019,27(1):11-13.

    HU Zongzhen,ZHAO Yanli. PID control based on self-adjusting BP neural network[J]. Computer and Information Technology,2019,27(1):11-13.
    [5] 郭伟东,李明,亢俊明,等. 基于机器视觉的矿井输煤系统优化节能控制[J]. 工矿自动化,2020,46(10):69-75.

    GUO Weidong,LI Ming,KANG Junming,et al. Optimal energy saving control of mine coal transportation system based on machine vision[J]. Industry and Mine Automation,2020,46(10):69-75.
    [6] WANG Guimei,LU Shenghui,LIU Jiehui,et al. Coal volume measurement of belt conveyor based on image processing[J]. Acta Metrologica Sinica,2020,41(6):724-728.
    [7] 曹松青,郝万君. 基于NMPC−PID的大型风电机组独立变桨距载荷控制[J]. 计算机应用与软件,2020,37(10):34-40.

    CAO Songqing,HAO Wanjun. Individual pitch load control of large wind turbines based on NMPC-PID[J]. Computer Applications and Software,2020,37(10):34-40.
    [8] 韩东升,杜永贵,庞宇松,等. 基于预见控制的带式输送机调速节能方法[J]. 工矿自动化,2018,44(6):64-68.

    HAN Dongsheng,DU Yonggui,PANG Yusong,et al. Speed regulation energy saving method of belt conveyor based on preview control[J]. Industry and Mine Automation,2018,44(6):64-68.
    [9] 张文静,曹博文,刘曰锋,等. 基于分数阶滑模自适应神经网络的中速磁浮列车运行控制方法[J]. 中国铁道科学,2022,43(2):152-160.

    ZHANG Wenjing,CAO Bowen,LIU Yuefeng,et al. Operation control method for medium-speed maglev trains based on fractional order sliding mode adaptive neural network[J]. China Railway Science,2022,43(2):152-160.
    [10] 龚桂荣. 皮带机巡检机器人控制系统设计与研究[D]. 徐州: 中国矿业大学, 2019.

    GONG Guirong. Design and research on an inspection robot control system for belt conveyor[D]. Xuzhou: China University of Mining and Technology, 2019.
    [11] 彭月,苏芷玄,杨杰,等. 基于PSO−BP−PID单点混合悬浮球控制算法研究[J]. 铁道科学与工程学报,2022,19(6):1511-1520.

    PENG Yue,SU Zhixuan,YANG jie,et al. On hybrid single-point magnetic levitation ball control algorithm based on BP-PID[J]. Journal of Railway Science and Engineering,2022,19(6):1511-1520.
    [12] 吉建华,苗长云,李现国,等. 基于PSO带式输送机PID控制器参数智能整定的适应度函数设计[J]. 机械工程学报,2022,58(3):1123-1129.

    JI Jianhua,MIAO Changyun,LI Xianguo,et al. Design of fitness function for intelligent parameter tuning of PID controller on belt conveyor with PSO[J]. Journal of Mechanical Engineering,2022,58(3):1123-1129.
    [13] 王卉. 基于模糊PID理论的带式输送机调速系统设计[J]. 煤矿机械,2019,40(9):14-16.

    WANG Hui. Design of speed regulation system for belt conveyor based on fuzzy PID theory[J]. Coal Mine Machinery,2019,40(9):14-16.
    [14] 曹江卫,魏霞. 基于RBF−PID控制器的带式输送机自适应调速系统[J]. 煤矿机械,2020,41(5):203-205.

    CAO Jiangwei,WEI Xia. Adaptive speed regulation system of belt conveyor based on RBF-PID controller[J]. Coal Mine Machinery,2020,41(5):203-205.
    [15] 李航,杜璠,胡晓兵,等. 改进的BP神经网络PID控制器在气体浓度控制中的研究[J]. 四川大学学报(自然科学版),2020,57(6):1103-1109.

    LI Hang,DU Fan,HU Xiaobing,et al. Research on improved BP neural network PID controller in gas concentration control[J]. Journal of Sichuan University(Natural Science Edition),2020,57(6):1103-1109.
    [16] 杨华伟,帕孜来•马合木提,张毅. PSO−BP−PID算法在双容水箱系统中的应用[J]. 电气传动,2017,47(5):78-80.

    YANG Huawei,PAZILAI Mahemuti,ZHANG Yi. Application of PSO-BP-PID algorithm in double tank water system[J]. Electric Drive,2017,47(5):78-80.
    [17] 朱馨渝,马平. 基于改进PSO−BP神经网络的PID参数优化方法[J]. 现代电子技术,2022,45(21):127-130.

    ZHU Xinyu,MA Ping. PID parameter optimization method based on improved PSO-BP neural network[J]. Modern Electronics Technique,2022,45(21):127-130.
    [18] SRINIVAS M,PATNAIK L M. Adaptive probabilities of crossover and mutation in genetic algorithms[J]. IEEE Transactions on Systems,Man,and Cybernetics,1994,24(4):656-667. doi: 10.1109/21.286385
    [19] GAO Chenyang, GAO Yuelin, LV Shanshan. Improved simulated annealing algorithm for flexible job shop scheduling problems[C]. Chinese Control and Decision Conference, Yinchuan, 2016: 2223-2228.
    [20] WANG Zheng,WANG Bo,LIU Chun,et al. Improved BP neural network algorithm to wind power forecast[J]. The Journal of Engineering,2017(13):940-943.
    [21] 郭彩杏,郭晓金,柏林江. 改进遗传模拟退火算法优化BP算法研究[J]. 小型微型计算机系统,2019,40(10):2063-2067.

    GUO Caixing,GUO Xiaojin,BAI Linjiang. Rsearch on improved BP algorithm for genetic simulated annealing algorithm[J]. Journal of Chinese Computer Systems,2019,40(10):2063-2067.
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出版历程
  • 收稿日期:  2022-08-21
  • 修回日期:  2023-05-15
  • 网络出版日期:  2023-05-22

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