Application of neural networks to decentralized control of robot manipulators with high degree of freedom

Author

Sadati, Nasser ; Elhamifar, Ehsan

Author_Institution

Intelligent Syst. Lab., Sharif Univ. of Technol., Tehran

fYear

2005

fDate

16-16 Nov. 2005

Lastpage

488

Abstract

In this paper, a neural network decentralized control for trajectory tracking of robot manipulators is developed. The proposed decentralized control allows the overall closed-loop system to be stabilized while making the tracking error to be uniformly ultimately bounded (UUB), without having any prior knowledge of the robot manipulator dynamics. The interconnections in the dynamic equations of each subsystem are considered with unknown nonlinear bounds. The RBF neural networks (RBFNNs) are proposed to model the unknown nonlinear dynamics of the robots and the interconnection terms. Using Lyapunov method, the stability of the overall system is investigated

Keywords

Lyapunov methods; closed loop systems; decentralised control; manipulator dynamics; neurocontrollers; position control; radial basis function networks; stability; Lyapunov method; RBF neural network; closed-loop system; degree of freedom; neural network decentralized control; nonlinear dynamics; robot manipulator dynamics; system stability; trajectory tracking; uniformly ultimately boundedness; Distributed control; Error correction; Lyapunov method; Manipulator dynamics; Neural networks; Nonlinear dynamical systems; Nonlinear equations; Robots; Stability; Trajectory;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, 2005. ICTAI 05. 17th IEEE International Conference on

Conference_Location

Hong Kong

ISSN

1082-3409

Print_ISBN

0-7695-2488-5

Type

conf

DOI

10.1109/ICTAI.2005.39

Filename

1562983