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Proceedings Paper

Manipulator trajectory control using neural networks: from application to theory and back again
Author(s): Yichuang Jin; A. G. Pipe; A. Winfield
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Paper Abstract

In this paper we first briefly review neural networks and some previous results of their applications on manipulator trajectory control. Then we go into the main part of the paper, i.e., theoretical analysis of neuro-manipulator control systems. It consists of control structure designs, off-line/on-line learning algorithms, and system stability proof. Two control structures are presented, both having a stability guarantee. Simulation on a Puma 560 robot and an experiment on a Mentor robot are also presented to demonstrate how to use the theoretical results and to evaluate performance of the developed control structures.

Paper Details

Date Published: 6 April 1995
PDF: 12 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205118
Show Author Affiliations
Yichuang Jin, Univ. of the West of England (United Kingdom)
A. G. Pipe, Univ. of the West of England (United Kingdom)
A. Winfield, Univ. of the West of England (United Kingdom)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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