Paper Info
Title
Recurrent fuzzy wavelet neural network variable impedance control of robotic manipulators with fuzzy gain dynamic surface in an unknown varied environment
Abstract
In this paper, an intelligent variable impedance control combined with a fuzzy gain dynamic surface is proposed to improve the interaction of the robot manipulator with an unknown varied environment. The parameters of the proposed variable impedance are adapted by optimization an introduced cost function using a recurrent fuzzy wavelet network. The stability conditions for the varying inertial, stiffness and damping are presented to guarantee the stability of the variable impedance. Additionally, a fuzzy dynamic surface method is developed to tune the gains of the dynamic surface as a robust controller. The proposed fuzzy gain dynamic surface is used to force the end-effector of the manipulator to track the desired impedance profile in the presence of large disturbances. Using Lyapunov's method, the stability of the mentioned closed-loop system is proved. Finally, by using a designed simulator for IRB120 (ABB) robot, several simulations are carried out to verify the performance of the proposed method for the execution of various tasks in an unknown varied environment in the presence of large disturbances.
Year
DOI
Venue
2021
10.1016/j.fss.2020.05.001
Fuzzy Sets and Systems
Keywords
DocType
Volume
Intelligent impedance control,Robotic manipulator,Recurrent fuzzy wavelet neural networks,Fuzzy dynamic surface control
Journal
416
ISSN
Citations 
PageRank 
0165-0114
1
0.39
References 
Authors
0
6
Name
Order
Citations
PageRank
Mohammad Hossein Hamedani110.72
Maryam Zekri2737.03
Farid Sheikholeslam311514.05
Mario Selvaggio4206.45
Fanny Ficuciello512216.80
B. Siciliano6857110.41