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dc.contributor.authorYoo, Kisuck
dc.contributor.authorThursby, Michael H.
dc.date.accessioned2017-10-06T16:51:11Z
dc.date.available2017-10-06T16:51:11Z
dc.date.issued1993-07-22
dc.identifier.citationYoo, K., & Thursby, M. H. (1993). Robust linear quadratic regulation using neural network. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 1919 157-161.en_US
dc.identifier.urihttp://hdl.handle.net/11141/1791
dc.descriptionAlgorithms, Least squares approximations, Lyapunov methods, Neural networks, Robustness (control systems)en_US
dc.description.abstractUsing 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∞ control theory. A Lyapunov equation is used to define stability in all cases.en_US
dc.language.isoen_USen_US
dc.rightsThis published article is made available in accordance with publishers policy. It may be subject to U.S. copyright law.en_US
dc.rights.urihttp://spie.org/publications/journals/guidelines-for-authors#Terms_of_Useen_US
dc.titleRobust linear quadratic regulation using neural networken_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1117/12.148406


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