DocumentCode
2316533
Title
PD control of robot with RBF networks compensation
Author
Yu, Wen ; Heredia, JoséAntonio
Author_Institution
Dept. de Control Autom., CINVESTAV-IPN, Mexico City, Mexico
Volume
5
fYear
2000
fDate
2000
Firstpage
329
Abstract
In this paper the popular PD controller of robot manipulator is modified. RBF neural networks are used to compensate the gravity and friction. No exact knowledge of the robot dynamics is required. The suggested learning law of neuro compensator is similar to the well-known backpropagation algorithm but with additional robust terms. Lyapunov-like analysis is used to derive the stability of learning algorithm
Keywords
Lyapunov matrix equations; backpropagation; compensation; manipulators; neurocontrollers; radial basis function networks; robust control; two-term control; Lyapunov-like analysis; PD control; RBF networks compensation; RBF neural networks; backpropagation algorithm; friction compensation; gravity ion; learning algorithm stability; neuro compensator; robot manipulator; robust terms; Algorithm design and analysis; Backpropagation algorithms; Friction; Gravity; Manipulator dynamics; Neural networks; PD control; Radial basis function networks; Robot control; Robustness;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.861488
Filename
861488
Link To Document