A modified PD control of robot manipulator using neural network compensator

Author

Huerta, Jos?© Antonio Heredia ; Yu, Wen

Author_Institution

Seccion de Control Autom., CINVESTAV-IPN, Mexico City, Mexico

Volume

3

fYear

1999

fDate

6/21/1905 12:00:00 AM

Firstpage

1999

Abstract

In this paper a modified PD-like controller for robot manipulator is proposed. Nonlinear compensation terms are added to the PD controller. Since we assume the dynamic of the robot are unknown, RBF neural networks are used to estimate them. The neural compensator does not need off-line learning. The suggested learning laws are similar to the well-known backpropagation algorithm but with some additional terms. A Lyupunov-like analysis is used to derive these stable learning laws, as well as to assured the stability of the closed-loop system

Keywords

closed loop systems; compensation; learning (artificial intelligence); manipulator dynamics; neurocontrollers; radial basis function networks; stability; two-term control; PD controller; closed-loop system; dynamics; learning laws; neural network; neurocontrol; nonlinear compensation; robot manipulator; stability; Asymptotic stability; Automatic control; Friction; Gravity; Manipulator dynamics; Neural networks; PD control; Robotics and automation; Robots; Transmission line matrix methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.832691

Filename

832691