DocumentCode
2012138
Title
Stable visual servoing with neural network compensation
Author
Loreto, Gerardo ; Yu, Wen ; Garrido, Ruben
Author_Institution
Departamento de Control Automatico, CINVESTAV-IPN, Mexico, Mexico
fYear
2001
fDate
2001
Firstpage
183
Lastpage
188
Abstract
We propose a stable 2D visual servoing algorithm for planar robot manipulators. We assume that gravity and friction are unknown and that there exists modeling errors in the vision system. By using a radial basis function neural network, it is shown that these uncertainties can be compensated. We prove that without or with unmodeled dynamics, the 2D visual servoing with neural networks compensation is Lyapunov stable
Keywords
Jacobian matrices; Lyapunov methods; closed loop systems; compensation; friction; manipulator dynamics; position control; recurrent neural nets; robot vision; stability; Lyapunov stability; modeling errors; neural network compensation; planar robot manipulators; radial basis function neural network; stable visual servoing; unmodeled dynamics; vision system; Friction; Gravity; Manipulator dynamics; Neural networks; PD control; Robot kinematics; Robot vision systems; Robotics and automation; Service robots; Visual servoing;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control, 2001. (ISIC '01). Proceedings of the 2001 IEEE International Symposium on
Conference_Location
Mexico City
ISSN
2158-9860
Print_ISBN
0-7803-6722-7
Type
conf
DOI
10.1109/ISIC.2001.971505
Filename
971505
Link To Document