Title :
Recurrent neural network control for nonlinear friction with sliding mode and friction estimator
Author :
Han, Seong Ik ; Lee, Kwon Soon ; Yeo, Dae Yeon ; Koo, Kyung Wan ; Kim, Sae Han ; Ahn, Woo Young
Author_Institution :
Dept. of Electr. Eng., Dong-A Univ., Busan, South Korea
Abstract :
In this paper, we have developed an friction compensation scheme for the rotary servo system using sliding mode and recurrent neural networks with friction estimator. An adaptive parameter updating rules of the recurrent neural network (RNN) and friction estimator have been developed to mimic the ideal sliding mode control and compensate the nonlinear friction via the Lyapunov stability theory.
Keywords :
Lyapunov methods; friction; neurocontrollers; nonlinear control systems; recurrent neural nets; servomechanisms; variable structure systems; Lyapunov stability; friction compensation; friction estimator; nonlinear friction; recurrent neural network control; rotary servo system; sliding mode; Approximation methods; Artificial neural networks; Friction; Recurrent neural networks; Servomotors; Uncertainty; Dynamic friction; LuGre friction mode; Recurrent neural network; Servo system; Sliding mode control; component;
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
DOI :
10.1109/ICNC.2010.5583904
