DocumentCode :
504764
Title :
Adaptive friction observer and sliding mode controller development with RFNN for nonlinear friction compensation
Author :
Han, Seong Ik ; Cho, Young Su ; Jin, Seong Min ; Lee, Chang Don ; Yang, Soon Yong
Author_Institution :
Dept. of Electr. Autom., Suncheon First Coll., Cheonnam, South Korea
fYear :
2009
fDate :
18-21 Aug. 2009
Firstpage :
4971
Lastpage :
4976
Abstract :
In this paper, the adaptive friction compensation schemes are developed to provide much enhanced position tracking performance against nonlinear dynamic friction. The adaptive friction parameter observer possessing a simple structure and to be easy to implementation into controller is first studied to estimate the friction parameters. The process of the uncertainty approximation using the RFNN technique is considered to enhance the positioning performance. Suppressing additional unknown friction uncertainty by the RFNN, the favorable position tracking result can be achieved via some simulation and experiment to the rotary servo mechanical system.
Keywords :
adaptive control; compensation; friction; fuzzy neural nets; machine control; neurocontrollers; nonlinear control systems; observers; recurrent neural nets; servomechanisms; uncertain systems; variable structure systems; adaptive friction compensation; adaptive friction parameter observer; friction parameter estimation; friction uncertainty; nonlinear friction compensation; position tracking; recurrent fuzzy neural network; rotary servo mechanical system; sliding mode controller; uncertainty approximation; Adaptive control; Control systems; Friction; Fuzzy control; Fuzzy neural networks; Hysteresis; Neural networks; Programmable control; Servomechanisms; Sliding mode control; Adaptive friction observer; LuGre friction model; Recurrent fuzzy neural networks; Sliding mode control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
ICCAS-SICE, 2009
Conference_Location :
Fukuoka
Print_ISBN :
978-4-907764-34-0
Electronic_ISBN :
978-4-907764-33-3
Type :
conf
Filename :
5334646
Link To Document :
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