DocumentCode
2649298
Title
Notice of Retraction
Robot friction compensation based on neural network
Author
Hongli Wang ; Xia Wang ; Yujun Tang
Author_Institution
Dept. of Math. & Inf. Sci., Tangshan Teachers´ Coll., Tangshan, China
Volume
7
fYear
2010
fDate
16-18 April 2010
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A new method to compensate the friction of robot manipulator has been proposed. Friction is a complex nonlinear force, it´s structure and parameter is hard to identify. Furthermore, friction model changes wit with many factors. A BP neural network is used to learn and compensate the nonlinear friction in the inner loop and its weight is adjusted on line while working. Thus, network can learn the changes at any moment. At the end of the article, we give simulation result, which verifies the method.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A new method to compensate the friction of robot manipulator has been proposed. Friction is a complex nonlinear force, it´s structure and parameter is hard to identify. Furthermore, friction model changes wit with many factors. A BP neural network is used to learn and compensate the nonlinear friction in the inner loop and its weight is adjusted on line while working. Thus, network can learn the changes at any moment. At the end of the article, we give simulation result, which verifies the method.
Keywords
backpropagation; manipulators; neural nets; BP neural network; nonlinear force; nonlinear friction; robot friction compensation; robot manipulator; Acceleration; Educational institutions; Educational robots; Electronic mail; Friction; Manipulators; Neural networks; Robot control; Torque; Uncertainty; Friction Compensation; Neural Network; Robot Control;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5485427
Filename
5485427
Link To Document