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Proceedings Paper

Learning control of robotic manipulators
Author(s): Heng-Ming Tai; Yu-Che Chen
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Paper Abstract

In this paper, we propose a learning control scheme for direct trajectory control of robotic manipulators. The main features are that we use a priori structure knowledge of robot dynamics in the design and the neural networks are not used to learn inverse dynamic models. The neural network controller is utilized to compensate the deviation due to the approximate models of robotic manipulators. In addition, true teaching signals of the neural network compensators are employed in the learning phase. Simulations are conducted to show the feasibility of the proposed method.

Paper Details

Date Published: 16 September 1992
PDF: 10 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140034
Show Author Affiliations
Heng-Ming Tai, Univ. of Tulsa (United States)
Yu-Che Chen, Univ. of Tulsa (United States)

Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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