Adaptive nonlinear control using input normalized neural networks

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dc.contributor.authorLeeghim, Henzeh-
dc.contributor.authorSeo, In-Ho-
dc.contributor.authorBang, Hyochoong-
dc.identifier.citationJournal of Mechanical Science and Technology, Vol.22, No.6, pp.1073-1083en
dc.description.abstractAn adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small.en
dc.description.sponsorshipThe present study was supported by National Research Lab. (NRL) Program (2002, M1-0203-00- 0006) by the Ministry of Science and Technology, Korea. Theen
dc.publisherSpringer Verlag (Germany)en
dc.subjectAdaptive nonlinear controlen
dc.subjectNeural networksen
dc.subjectInput normalizationen
dc.subjectFeedback Linearizationen
dc.subjectUncertain systemsen
dc.titleAdaptive nonlinear control using input normalized neural networksen


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