STUDY OF FRICTION COMPENSATION MODEL FOR MOBILE ROBOT’S JOINTS

被引：0
|
作者：
Cao, Yanjie [1 ]
Nor, Norzalilah Mohamad [1 ]
Samad, Zahurin [1 ]
机构：
[1] School of Mechanical Engineering, Universiti Sains Malaysia (USM), Penang, Malaysia
来源：
Facta Universitatis, Series: Mechanical Engineering | 2024年 / 22卷 / 04期
关键词：
Frictional forces inside the joints of mobile robots hurt robot operation's stability and positioning accuracy. Therefore; establishing a suitable friction force compensation model has been a hot research topic in robotics. To explore the robot joint friction compensation model; three friction compensation models: linear; nonlinear; and neural network models; are developed in this paper. Based on the deep learning algorithm for three models at low speed; high speed; acceleration; and uniform speed training test; respectively results have been obtained. The test results show that the best friction compensation effect comes from combining neural network models in acceleration and a consistent speed state way. The friction compensation model trained this way yielded superior results to the other combinations tested. Finally; using the method; a friction compensation model trained by adding a neural network to the feedforward control torque was tested on a four-wheeled mobile robot platform. The test results show that the relative error of the torque caused by the friction of each joint is reduced by 15%-75% in 8 groups of tests; which indicates that our friction compensation method has a positive effect on improving the accuracy of the joint torque. © 2024 by University of Niš; Serbia;
D O I：
10.22190/FUME230605039C
中图分类号：
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页码：721 / 740
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