基于提升力控制和滑膜控制器的智能索道式换线机器人设计方法

J. Robotics Pub Date : 2022-04-12 DOI:10.1155/2022/3640851
Jiazhen Duan, Ruxin Shi, Hongtao Liu, Hailong Rong
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引用次数: 2

摘要

针对旧线路拆除人工施工效率低、可靠性差、安全性差的问题,提出了一种基于升降力控制和滑膜控制器的智能索道式换线机器人的设计方法。首先,建立机器人载荷与线路垂度的力学模型,利用机器人所在架空线路的垂度计算顶升装置需要提供给机器人的顶推力。然后,将径向基函数(RBF)神经网络自适应算法引入滑膜控制器,设计了一种基于RBF神经网络的自适应滑模位置控制算法,实现了机器人在复杂操作环境下的高精度运动控制。最后,基于机器人的紧凑性、重量性和可靠性,从拓扑、尺寸、形状和形态四个方面进行优化设计,提出了机器人的拆线设计方案,并制作出机器人。通过仿真实验,对所研制的机器人与其他三种机器人在相同条件下进行了对比分析。结果表明,本研究开发的机器人最大作业时间为45 min,最大爬坡角度为10°,最大行走速度为1 m/s,均为最优,性能优于其他三种比较算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design Method of Intelligent Ropeway Type Line Changing Robot Based on Lifting Force Control and Synovial Film Controller
Aiming at the problems of low efficiency, reliability, and safety of manual construction for demolition of old lines, a design method of an intelligent ropeway type line changing robot based on lifting force control and synovial film controller is proposed. First, the mechanical model of robot load and line sag is established, and the sag of the overhead line where the robot is located is used to calculate the jacking force that the jacking device needs to provide to the robot. Then, by introducing the radial basis function (RBF) neural network adaptive algorithm into the synovial controller, an adaptive sliding mode position control algorithm based on the RBF neural network is designed to achieve high-precision motion control of the robot in complex operating environments. Finally, based on the compactness, weight, and reliability of the robot, the optimal design is carried out from four aspects of topology, size, shape and morphology, and the design scheme of the robot for wire removal is proposed, and the robot is produced. The developed robot and the other three robots are compared and analyzed under the same conditions through simulation experiments. The results show that the maximum operating time, maximum climbing angle, and maximum traveling speed of the robot developed in this study are all optimal, which are 45 min, 10°, and 1 m/s respectively, and the performance is better than the other three comparison algorithms.
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