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Proceedings Paper

Real-time stable adaptive control implementation using a neural network processor
Author(s): Timothy Robinson; Mohammad Bodruzzaman; Kevin L. Priddy; Karl Mathia
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Paper Abstract

Helicopters are highly non-linear systems that have dynamics that change significantly with respect to environmental conditions. The system parameters also vary heavily with respect to velocity. These nonlinearities limit the use of traditional fixed controllers, since they can make the aircraft unstable. The purpose of this paper is to make contributions to the development of an `intelligent' control system that can be applied to complex problems such as this in real- time. Using a slowly changing model and a simplified nonlinear model as examples, a neural network based controller is shown to have the ability to learn from these example plants and to generalize this knowledge for previously unseen plants. The adaptability comes from a neural network that adjusts coefficients of the controller in real-time while running on the accurate automation neural network processor.

Paper Details

Date Published: 6 April 1995
PDF: 6 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205122
Show Author Affiliations
Timothy Robinson, Tennessee State Univ. (United States)
Mohammad Bodruzzaman, Tennessee State Univ. (United States)
Kevin L. Priddy, Accurate Automation Corp. (United States)
Karl Mathia, Accurate Automation Corp. (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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