A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems

A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems

A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot ma...

Full description

Bibliographic Details
Main Authors: Yong Tao, Jiaqi Zheng, Yuanchang Lin
Format: Article
Language:English
Published: SAGE Publishing 2016-01-01
Series:International Journal of Advanced Robotic Systems
Online Access:https://doi.org/10.5772/62002
id doaj-91eabdc07888405ca128f2a8616a1d8a
record_format Article
spelling doaj-91eabdc07888405ca128f2a8616a1d8a2020-11-25T03:09:34ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142016-01-011310.5772/6200210.5772_62002A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic SystemsYong Tao0Jiaqi Zheng1Yuanchang Lin2 Beihang University, Beijing, China School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan, China Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, ChinaA sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network parameters are derived by a Koski function algorithm to ensure the network convergences and enacts stable control. The simulations and experimental results of the deburring robot system are provided to illustrate the effectiveness of the proposed RBFNN-SMC control method. The advantages of the proposed RBFNN-SMC method are also evaluated by comparing it to existing control schemes.https://doi.org/10.5772/62002
collection DOAJ
language English
format Article
sources DOAJ
author Yong Tao
Jiaqi Zheng
Yuanchang Lin
spellingShingle Yong Tao
Jiaqi Zheng
Yuanchang Lin
A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems
International Journal of Advanced Robotic Systems
author_facet Yong Tao
Jiaqi Zheng
Yuanchang Lin
author_sort Yong Tao
title A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems
title_short A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems
title_full A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems
title_fullStr A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems
title_full_unstemmed A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems
title_sort sliding mode control-based on a rbf neural network for deburring industry robotic systems
publisher SAGE Publishing
series International Journal of Advanced Robotic Systems
issn 1729-8814
publishDate 2016-01-01
description A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network parameters are derived by a Koski function algorithm to ensure the network convergences and enacts stable control. The simulations and experimental results of the deburring robot system are provided to illustrate the effectiveness of the proposed RBFNN-SMC control method. The advantages of the proposed RBFNN-SMC method are also evaluated by comparing it to existing control schemes.
url https://doi.org/10.5772/62002
work_keys_str_mv AT yongtao aslidingmodecontrolbasedonarbfneuralnetworkfordeburringindustryroboticsystems
AT jiaqizheng aslidingmodecontrolbasedonarbfneuralnetworkfordeburringindustryroboticsystems
AT yuanchanglin aslidingmodecontrolbasedonarbfneuralnetworkfordeburringindustryroboticsystems
AT yongtao slidingmodecontrolbasedonarbfneuralnetworkfordeburringindustryroboticsystems
AT jiaqizheng slidingmodecontrolbasedonarbfneuralnetworkfordeburringindustryroboticsystems
AT yuanchanglin slidingmodecontrolbasedonarbfneuralnetworkfordeburringindustryroboticsystems
_version_ 1724661917163716608

Similar Items