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 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): 1−11
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): 1−11 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): 1−11

## 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)
###### 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　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. He is currently an associate professor at the College of Electrical Engineering, Sichuan University. 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　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. He is currently a professor at the College of Electrical Engineering, Sichuan University. His research interest covers advanced control methods and intelligent signal processing, power-electronics system and its control, motion control and robotic control GUO Rui　Received his bachelor, master, and Ph.D. degrees of mechanical engineering from Harbin Institute of Technology, China in 2001, 2003 and 2007, respectively. He is currently a professor of engineering at the State Grid Shandong Electric Power Company, China. His research interest covers advanced control methods and intelligent robot for power industry LI Sheng-Chuan　Graduated from Harbin University of Technology in 1991. He is currently a professor of engineering at the Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd., China. His research interest covers operation and maintenance of substation equipment and application of artificial intelligence in power grid FANG Hong-Wei　Received his bachelor degree from Wuhan Polytechnic University of Information Technology in 2017. Now he is pursuing his master degree in control theory and control engineering from Sichuan University. His research interest covers fuzzy control, adaptive dynamic programming and their applications HAN Ji-Xia　Received her bachelor degree from Zhengzhou University in 2017. Now she is pursuing her master degree in control theory and control engineering from 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

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##### 出版历程
• 收稿日期:  2019-04-17
• 录用日期:  2019-06-24
• 网络出版日期:  2022-01-12

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