A dynamic neural network for adaptive optimal learning of robot motion with guaranteed convergence rate

Author

Chung, Chae-Wook ; Lee, Hyun-Bae ; Kuc, Tae-Yong ; Yi, Taek-Chong

Author_Institution

Dept. of Electron. Eng., Sung Kyun Kwan Univ., Suwon, South Korea

Volume

2

fYear

1996

fDate

14-17 Oct 1996

Firstpage

1315

Abstract

This paper presents an optimal learning controller for uncertain robot systems which makes use of simple dynamic neural network units to estimate uncertain parameters and learn the unknown desired optimal input. With the aid of a Lyapunov function, it is shown that all the error signals in the system are bounded and the robot trajectory converges to the desired one globally and exponentially. The effectiveness of the proposed controller is shown by applying the controller to a planar robot manipulator

Keywords

adaptive control; intelligent control; motion control; neurocontrollers; optimal control; parameter estimation; robot dynamics; uncertain systems; Lyapunov function; adaptive optimal learning; convergence rate; dynamic neural network; parameter estimation; robot motion; uncertain robot systems; Adaptive systems; Biological neural networks; Control systems; Convergence; Manipulator dynamics; Neural networks; Neurofeedback; Nonlinear dynamical systems; Optimal control; Robot motion;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1996., IEEE International Conference on

Conference_Location

Beijing

ISSN

1062-922X

Print_ISBN

0-7803-3280-6

Type

conf

DOI

10.1109/ICSMC.1996.571301

Filename

571301