Robust friction state observer and recurrent fuzzy neural network design for dynamic friction compensation with backstepping control

Mechatronics ◽  
2010 ◽  
Vol 20 (3) ◽  
pp. 384-401 ◽  
Author(s):  
Seong Ik Han ◽  
Kwon Soon Lee
Keyword(s):  
Neural Network ◽  
Network Design ◽  
Fuzzy Neural Network ◽  
State Observer ◽  
Backstepping Control ◽  
Friction Compensation ◽  
Dynamic Friction ◽  
Fuzzy Neural ◽  
Recurrent Fuzzy Neural Network

Adaptive dynamic friction observer and recurrent fuzzy neural network estimator design with backstepping control

2009 ◽  
Vol 223 (7) ◽  
pp. 885-900 ◽  
Author(s):  
S I Han ◽  
K S Lee
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Friction Model ◽  
Dynamic Friction ◽  
Adaptive Dynamic ◽  
Lugre Friction Model ◽  
Fuzzy Neural ◽  
Friction Observer ◽  
Friction Parameters ◽  
Recurrent Fuzzy Neural Network

In this study, robust non-linear dynamic friction control is considered using a dynamic friction observer and intelligent control. An adaptive dynamic friction observer based on the LuGre friction model is proposed to estimate the friction parameters and a directly immeasurable friction state variable. A recurrent fuzzy neural network (RFNN) approximator and reconstructed error compensator are also designed to give additional robustness to the control system under friction model uncertainty. A proposed composite control scheme with a basic backstepping controller is applied to the position tracking control of the servo system. The performances of the proposed friction observer and the friction controller are demonstrated by some simulations and experiments.

Design of fuzzy system-fuzzy neural network-backstepping control for complex robot system

2021 ◽  
Vol 546 ◽  
pp. 1230-1255
Author(s):  
Kunming Zheng ◽  
Qiuju Zhang ◽  
Youmin Hu ◽  
Bo Wu
Keyword(s):  
Neural Network ◽  
Fuzzy System ◽  
Fuzzy Neural Network ◽  
Backstepping Control ◽  
Robot System ◽  
Fuzzy Neural
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