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
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;
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
DOI :
10.1109/NAFIPS.2008.4531205
