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dc.contributor.authorSanchez, E.N.
dc.contributor.authorAlanis, A.Y.
dc.contributor.authorLoukianov, A.G.
dc.description.abstractThe first designed robust direct neural control scheme is based on the backstepping technique, approximated by a high order neural network. On the basis of the Lyapunov approach, the respective stability analysis, for the whole closed-loop system, including the extended Kalman filter (EKF)-based NN learning algorithm, is also performed. The second robust indirect control is designed with a recurrent high order neural network, which enables to identify the plant model. A strategy to avoid specific adaptive weights zero-crossing and conserve the identifier controllability property is proposed. Based on this neural identifier and applying the discrete-time block control approach, a nonlinear sliding manifold with a desired asymptotically stable motions was formulated. Using a Lyapunov functions approach, a discrete-time sliding mode control that makes the designed sliding manifold to be attractive was introduced. � 2008 Springer-Verlag Berlin Heidelberg.
dc.titleConclusions and future work
dc.relation.ispartofjournalStudies in Computational Intelligence
dc.contributor.affiliationSanchez, E.N., CINVESTAV, Unidad Guadalajara, Plaza la Luna, Guadalajara, Jalisco 45091, Mexico; Alan�s, A.Y., Departamento Deciencias Computacionales, CUCEI, Universidad de Guadalajara, Blvd. Marcelino Garcia Barragan 1421, Jalisco 44430, Mexico; Loukianov, A.G., CINVESTAV, Unidad Guadalajara, Plaza la Luna, Guadalajara, Jalisco 45091, Mexico
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