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Proceedings Paper

Hardware implementation of a neural network controller for a manipulator arm
Author(s): Rosalyn S. Hobson; Rafael M. Inigo
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Paper Abstract

In a previous paper, a neural network with a reward/punish learning scheme controller for a manipulator arm was described. The inputs to the torque-generating neuron are the position error and the velocity of the joints. The output of the neuron is the torque required to control the arm to its desired position. The reward/punish learning mechanism is implemented to adaptively modify the weights. The neural network controller does not need a dynamic model of the arm. The dynamics are learned through training. In this paper we describe the hardware/software implementation of the neural network to control the shoulder joint of a Mitsubishi RM501 arm. Once the system was checked for correct operation the following tests were performed: (1) training the arm to hold is position at different angles (10, 40, 70, 100 and 120 degrees). The angle was to hold with very small error, even in the presence of significant disturbances, after a training period that varied from 3 to 12 seconds. (2) Training the arm at 50 degrees and then commanding it to follow a cosine trajectory from 50 to 70 degrees. The maximum error in this test was less than 1% of the desired value.

Paper Details

Date Published: 6 April 1995
PDF: 12 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205093
Show Author Affiliations
Rosalyn S. Hobson, Univ. of Virginia (United States)
Rafael M. Inigo, Univ. of Virginia (United States)

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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