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Proceedings Paper

Robust linear quadratic regulation using neural network
Author(s): Kisuck Yoo; Michael H. Thursby
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Paper Abstract

Using an Artificial Neural Network (ANN) trained with the Least Mean Square (LMS) algorithm we have designed a robust linear quadratic regulator for a range of plant uncertainty. Since there is a trade-off between performance and robustness in the conventional design techniques, we propose a design technique to provide the best mix of robustness and performance. Our approach is to provide different control strategies for different levels of uncertainty. We describe how to measure these uncertainties. We will compare our multiple strategies results with those of conventional techniques e.g. H(infinity ) control theory. A Lyapunov equation is used to define stability in all cases.

Paper Details

Date Published: 22 July 1993
PDF: 5 pages
Proc. SPIE 1919, Smart Structures and Materials 1993: Mathematics in Smart Structures, (22 July 1993); doi: 10.1117/12.148406
Show Author Affiliations
Kisuck Yoo, Florida Institute of Technology (United States)
Michael H. Thursby, Florida Institute of Technology (United States)

Published in SPIE Proceedings Vol. 1919:
Smart Structures and Materials 1993: Mathematics in Smart Structures
H. Thomas Banks, Editor(s)

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