多自由度机械臂运动控制研究  

Research on Motion Control of Multi Degree of Freedom Robotic Arm

作　　者：张平社 何志伟 ZHANG Pingshe;HE Zhiwei(Northwest Yongxin Group Co.,Ltd.,Lanzhou 730000,China;Northwest Yongxin Real Estate Co.,Ltd.,Lanzhou 730000,China)

机构地区：[1]西北永新集团有限公司,甘肃兰州730000 [2]西北永新置业有限公司,甘肃兰州730000

出　　处：《科技创新与生产力》2025年第3期100-102,共3页Sci-tech Innovation and Productivity

摘　　要：为提高汽车涂料生产中机械臂的工作效率,达到对多自由度机械臂的控制效果,基于径向基函数(RBF)神经网络,设计了一种新的运动控制方法。首先,根据运动学以及逆运动学理论,应用牛顿-欧拉方程构建机械臂运动模型,分析多自由度机械臂运动状态;然后,以高斯函数的形式表示RBF,搭建神经网络模型,并使用批处理学习算法完成RBF神经网络模型训练;最后,采用RBF神经网络逼近方法,构建控制器,再结合李雅普诺夫函数设置控制器的运行函数,完成控制过程。实验结果表明:该方法的控制能力与控制效率均优于当前其他方法。In order to improve the working efficiency of robotic arms in automotive paint production and achieve control over multi degree of freedom robotic arms,a new motion control method based on radial basis function(RBF)neural network is designed.Firstly,based on the theories of kinematics and inverse kinematics,the Newton-Euler equation is applied to construct the motion model of the robotic arm and analyze the motion state of the multi degree of freedom robotic arm;then,the RBF is represented in the form of a Gaussian function,a neural network model is built,and batch learning algorithms are used to complete the training of the RBF neural network model;Finally,the RBF neural network approximation method is used to construct the controller,and the Lyapunov function is combined to set the operating function of the controller to complete the control process.The experimental results show that the control capability and efficiency of this method are superior to other current methods.

关 键 词：多自由度机械臂 运动控制 数字控制 神经网络 径向基函数 MATLAB仿真 

分 类 号：TP241.2[自动化与计算机技术—检测技术与自动化装置] TP242.2[自动化与计算机技术—控制科学与工程] TP273TP183TP391.9