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基于FPSO的电力巡检机器人的广义二型模糊逻辑控制

吴庆 赵涛 佃松宜 郭锐 李胜川 方红帏 韩吉霞

吴庆, 赵涛, 佃松宜, 郭锐, 李胜川, 方红帏, 韩吉霞. 基于FPSO的电力巡检机器人的广义二型模糊逻辑控制. 自动化学报, 2022, 48(6): 1482−1492 doi: 10.16383/j.aas.c190306
引用本文: 吴庆, 赵涛, 佃松宜, 郭锐, 李胜川, 方红帏, 韩吉霞. 基于FPSO的电力巡检机器人的广义二型模糊逻辑控制. 自动化学报, 2022, 48(6): 1482−1492 doi: 10.16383/j.aas.c190306
Wu Qing, Zhao Tao, Dian Song-Yi, Guo Rui, Li Sheng-Chuan, Fang Hong-Wei, Han Ji-Xia. General type-2 fuzzy logic control for a power-line inspection robot based on FPSO. Acta Automatica Sinica, 2022, 48(6): 1482−1492 doi: 10.16383/j.aas.c190306
Citation: Wu Qing, Zhao Tao, Dian Song-Yi, Guo Rui, Li Sheng-Chuan, Fang Hong-Wei, Han Ji-Xia. General type-2 fuzzy logic control for a power-line inspection robot based on FPSO. Acta Automatica Sinica, 2022, 48(6): 1482−1492 doi: 10.16383/j.aas.c190306

基于FPSO的电力巡检机器人的广义二型模糊逻辑控制

doi: 10.16383/j.aas.c190306
基金项目: 国家重点研发计划(2018YFB1307401), 国家自然科学基金(61703291)资助
详细信息
    作者简介:

    吴庆:四川大学控制工程专业硕士研究生. 主要研究方向为模糊控制及其应用. E-mail: 2017223035223@stu.scu.edu.cn

    赵涛:四川大学电气工程学院副教授. 2010年获得西南交通大学学士学位. 2015年获得西南交通大学博士学位. 主要研究方向为二型模糊集理论和系统设计, 粗糙集和智能控制. 本文通信作者. E-mail: zhaotaozhaogang@126.com

    佃松宜:四川大学电气工程学院教授. 分别于1996和2002年获得四川大学学士和硕士学位. 2009年获得日本东本大学博士学位. 主要研究方向为先进控制理论和智能信号处理, 电力电子系统及其控制, 运动控制和机器人控制. E-mail: scudiansy@scu.edu.cn

    郭锐:国家电网山东电力公司教授级高级工程师. 分别于2001, 2003和2007年获得哈尔滨工业大学机械工程专业学士, 硕士和博士学位. 主要研究方向为先进控制理论和电力工业智能机器人. E-mail: guoruihit@gmail.com

    李胜川:国网辽宁省电力有限公司电力科学研究院教授级高级工程师. 1991年毕业于哈尔滨工业大学. 主要研究方向为变电站设备的运行和维护以及人工智能在电网中的应用. E-mail: lnlsc@163.com

    方红帏:四川大学控制理论与控制工程专业硕士研究生. 主要研究方向为模糊控制和自适应动态规划及其应用. E-mail: weihongfang528@163.com

    韩吉霞:四川大学控制理论与控制工程专业硕士研究生. 主要研究方向为模糊控制和滑模控制及其应用. E-mail: jixiahan@126.com

General Type-2 Fuzzy Logic Control for a Power-line Inspection Robot Based on FPSO

Funds: Supported by National Key Research and Development Program of China (2018YFB1307401) and Nationl Natural Science Foundation of China (61703291)
More Information
    Author Bio:

    WU Qing Master student in control engineering at Sichuan University. His research interest covers fuzzy control, intelligent control and their applications

    ZHAO Tao Associate professor at the College of Electrical Engineering, Sichuan University. He received his bachelor degree in mathematics and applied mathematics and his Ph.D. degree in systems engineering from Southwest Jiaotong University, in 2010 and 2015, respectively. His research interest covers type-2 fuzzy set theory and system design, rough sets, and intelligent control. Corresponding author of this paper

    DIAN Song-Yi Professor at the College of Electrical Engineering, Sichuan University. He received his bachelor and master degrees of control engineering from Sichuan University in 1996 and 2002, respectively. He received his Ph.D. degree in nanomechanics engineering from Tohoku University, Japan in 2009. His research interest covers advanced control methods and intelligent signal processing, power-electronics system and its control, motion control, and robotic control

    GUO Rui Professor of engineering at the State Grid Shandong Electric Power Company. He received his bachelor, master, and Ph.D. degrees of mechanical engineering from Harbin Institute of Technology in 2001, 2003, and 2007, respectively. His research interest covers advanced control methods and intelligent robot for power industry

    LI Sheng-Chuan Professor of engineering at the Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd.. He graduated from Harbin University of Technology in 1991. His research interest covers operation and maintenance of substation equipment and application of artificial intelligence in power grid

    FANG Hong-Wei Master student in control theory and control engineering at Sichuan University. His research interest covers fuzzy control, adaptive dynamic programming and their applications

    HAN Ji-Xia Master student in control theory and control engineering at Sichuan University. Her research interest covers fuzzy control, sliding mode control and their applications

  • 摘要: 针对电力巡检机器人(Power-line inspection robot, PLIR)的平衡调节问题, 设计了广义二型模糊逻辑控制器(General type-2 fuzzy logic controller, GT2FLC); 针对GT2FLC中隶属函数参数难以确定的问题, 通过模糊粒子群(Fuzzy particle swarm optimization, FPSO)算法来优化隶属函数参数. 将GT2FLC的控制性能与区间二型模糊逻辑控制器(Interval type-2 fuzzy logic controller, IT2FLC)和一型模糊逻辑控制器(Type-1 fuzzy logic controller, T1FLC) 的控制性能进行对比. 除此之外, 还考虑了外部干扰对三种控制器控制效果的影响. 仿真结果表明, GT2FLC具有更好的性能和处理不确定性的能力.
  • 图  1  PLIR模型

    Fig.  1  The model of PLIR

    图  2  广义二型模糊集

    Fig.  2  General type-2 fuzzy set

    图  3  不确定的迹

    Fig.  3  The footprint of uncertain

    图  4  Nite对应的隶属函数

    Fig.  4  The membership function for Nite

    图  5  Nfit对应的隶属函数

    Fig.  5  The membership function for Nfit

    图  6  PLIR平衡控制和优化原理图

    Fig.  6  The diagram of balance control and optimization for the PLIR

    图  7  FPSO算法流程图

    Fig.  7  The flow diagram of the FPSO algorithm

    图  8  优化前${{\tilde \theta }_1}$对应的FOU

    Fig.  8  The FOU for ${{\tilde \theta }_1}$ without optimization

    图  9  优化前${{{\dot{\tilde{\theta }}}}_{2}}$对应的FOU

    Fig.  9  FOU for ${{{\dot{\tilde{\theta }}}}_{2}}$ without optimization

    图  10  优化后${{\tilde \theta }_1}$对应的FOU

    Fig.  10  The FOU for ${{\tilde \theta }_1}$ with optimization

    图  11  优化后${{{\dot{\tilde{\theta }}}}_{2}}$对应的FOU

    Fig.  11  The FOU for ${{{\dot{\tilde{\theta }}}}_{2}}$ with optimization

    图  12  无干扰下$\theta_1$$\dot{\theta}_1$的响应

    Fig.  12  Responses of $\theta_1$ and $\dot{\theta}_1$ without disturbance

    图  13  无干扰下$\theta_2$$\dot{\theta}_2$的响应

    Fig.  13  Responses of $\theta_2$ and $\dot{\theta}_2$ without disturbance

    图  14  有干扰下$\theta_1$$\dot{\theta}_1$的响应

    Fig.  14  Responses of $\theta_1$ and $\dot {\theta}_1$ with disturbances

    图  15  有干扰下$\theta_2$$\dot{\theta}_2$的响应

    Fig.  15  Responses of $\theta_2$ and $\dot{\theta}_2$ with disturbances

    表  1  PLIR对应参数值

    Table  1  Values of parameters for the PLI robot

    参数 参数值   参数 参数值
    $m_1\, ({\rm{kg} })$ 63   $m_2\,({\rm{kg} })$ 27
    $h_1\,({\rm{m} })$ 0.18   $h_{20}\,({\rm{m} })$ 0.42
    $l \,({\rm{m} })$ 0.5   $h\,({\rm{m} })$ 0.5
    下载: 导出CSV

    表  2  FPSO惯性权重调整模糊规则表

    Table  2  The rulebase of adjustment for inertia weight in FPSO

    $\omega$ $Nite$
    NB NS ZO PS PB
    $Nfit$ NB ZO PS PS PB PB
    NS NS ZO PS PB PB
    ZO NS NS ZO PS PS
    PS NB NB NS ZO PS
    PB NB NB NS NS ZO
    下载: 导出CSV

    表  3  PLIR平衡调节模糊规则表

    Table  3  The rulebase of balance adjustment for the PLIR

    u2 ${{{\tilde \theta }_1}}$
    NB NS ZO PS PB
    ${{{\dot{\tilde{\theta }}}}_{1}}$ NB PB PB PS PS ZO
    NS PB PB PS ZO NS
    ZO PS PS ZO NS NS
    PS PS ZO NS NB NB
    PB ZO NS NS NB NB
    下载: 导出CSV

    表  4  无干扰下平均评价指标

    Table  4  Average evaluation index without disturbance

    控制器 ISE IAE ITAE
    T1FLC-PSO 0.02660 0.14820 0.08061
    IT2FLC-PSO 0.02655 0.14238 0.06800
    GT2FLC-PSO 0.02655 0.14290 0.06914
    T1FLC-FPSO 0.02656 0.14534 0.07417
    IT2FLC-FPSO 0.02653 0.14236 0.06732
    GT2FLC-FPSO 0.02654 0.14127 0.06536
    下载: 导出CSV

    表  5  有干扰下平均评价指标

    Table  5  Average evaluation index with disturbances

    控制器 ISE IAE ITAE
    T1FLC-PSO 0.07380 0.39140 1.76303
    IT2FLC-PSO 0.06907 0.37776 1.69851
    GT2FLC-PSO 0.06856 0.37537 1.68996
    T1FLC-FPSO 0.07376 0.38991 1.75956
    IT2FLC-FPSO 0.06875 0.37773 1.69275
    GT2FLC-FPSO 0.06857 0.37414 1.68423
    下载: 导出CSV
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  • 收稿日期:  2019-04-17
  • 录用日期:  2019-06-24
  • 网络出版日期:  2022-01-12
  • 刊出日期:  2022-06-02

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