DocumentCode
1601807
Title
Joint friction identification for robots using TSK fuzzy system based on subtractive clustering
Author
Qin, Zhongkai ; Ren, Qun ; Baron, Luc ; Balazinski, Marek ; Birglen, Lionel
Author_Institution
Dept. of Mech. Eng., Ecole Polytech. de Montreal, Montreal, QC
fYear
2008
Firstpage
1
Lastpage
6
Abstract
In this paper, the joint friction of a robotic manipulatoris identified by using subtractive clustering based Takagi-Sugeno-Kang (TSK) fuzzy logic system (FLS). The proposed approach can provide accurate prediction of the joint friction despite the nonlinearity of the friction and measurement uncertainty. Simulation results show the effectiveness and convenience of the method.
Keywords
control nonlinearities; friction; fuzzy control; manipulator dynamics; TSK fuzzy system; Takagi-Sugeno-Kang fuzzy logic system; friction nonlinearity; joint friction identification; measurement uncertainty; robotic manipulator; subtractive clustering; Actuators; Algorithm design and analysis; Computational modeling; Friction; Fuzzy logic; Fuzzy systems; Manipulator dynamics; Robotic assembly; Robots; Takagi-Sugeno-Kang model;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Information Processing Society, 2008. NAFIPS 2008. Annual Meeting of the North American
Conference_Location
New York City, NY
Print_ISBN
978-1-4244-2351-4
Electronic_ISBN
978-1-4244-2352-1
Type
conf
DOI
10.1109/NAFIPS.2008.4531205
Filename
4531205
Link To Document