Robust linear quadratic regulation using neural network

Kisuck Yoo; Michael H. Thursby

doi:10.1117/12.148406

22 July 1993 Robust linear quadratic regulation using neural network

Kisuck Yoo, Michael H. Thursby

Proceedings Volume 1919, Smart Structures and Materials 1993: Mathematics in Smart Structures; (1993) https://doi.org/10.1117/12.148406
Event: 1993 North American Conference on Smart Structures and Materials, 1993, Albuquerque, NM, United States

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.

Citation Download Citation

Kisuck Yoo and Michael H. Thursby "Robust linear quadratic regulation using neural network", Proc. SPIE 1919, Smart Structures and Materials 1993: Mathematics in Smart Structures, (22 July 1993); https://doi.org/10.1117/12.148406

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