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Proceedings Paper

DC motor speed control using neural networks
Author(s): Heng-Ming Tai; Junli Wang; Ashenayi Kaveh
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Paper Abstract

This paper presents a scheme that uses a feedforward neural network for the learning and generalization of the dynamic characteristics for the starting of a dc motor. The goal is to build an intelligent motor starter which has a versatility equivalent to that possessed by a human operator. To attain a fast and safe starting from stall for a dc motor a maximum armature current should be maintained during the starting period. This can be achieved by properly adjusting the armature voltage. The network is trained to learn the inverse dynamics of the motor starting characteristics and outputs a proper armature voltage. Simulation was performed to demonstrate the feasibility and effectiveness of the model. This study also addresses the network performance as a function of the number of hidden units and the number of training samples. 1.

Paper Details

Date Published: 1 August 1990
PDF: 9 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21182
Show Author Affiliations
Heng-Ming Tai, Univ. of Tulsa (United States)
Junli Wang, Univ. of Tulsa (United States)
Ashenayi Kaveh, Univ. of Tulsa (United States)

Published in SPIE Proceedings Vol. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

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