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
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;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.861488
