Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12104/67447
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dc.contributor.authorLopez-Franco, M.
dc.contributor.authorSanchez, E.N.
dc.contributor.authorAlanis, A.Y.
dc.contributor.authorArana-Daniel, N.
dc.date.accessioned2015-11-19T18:52:11Z-
dc.date.available2015-11-19T18:52:11Z-
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/20.500.12104/67447-
dc.description.abstractThis paper deals with a decentralized inverse optimal neural controller for MIMO discrete-time unknown nonlinear systems, in a presence of external disturbances and parameter uncertainties. It uses two techniques: first, an identifier based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF) algorithm; second, on the basis of the real identifier a controller which uses inverse optimal control, is designed to avoid solving the Hamilton Jacobi Bellman (HJB) equation. The proposed scheme is implemented in real-time to control a Shrimp robot. © 2013 IEEE.
dc.titleReal-time decentralized inverse optimal neural control for a Shrimp robot
dc.typeConference Paper
dc.identifier.doi10.1109/IJCNN.2013.6706785
dc.relation.ispartofjournalProceedings of the International Joint Conference on Neural Networks
dc.subject.keywordDecentralized Inverse Optimal Neural Control; Mobile Robots; Neural Control; Neural identifier; Recurrent High Order Neural Networks
dc.contributor.affiliationLopez-Franco, M., CINVESTAV, Unidad Guadalajara, Jalisco 45015, Mexico; Sanchez, E.N., CINVESTAV, Unidad Guadalajara, Jalisco 45015, Mexico; Alanis, A.Y., CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Col. las aguilas, C.P. 45080, Zapopan, Jalisco, Mexico; Arana-Daniel, N., CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Col. las aguilas, C.P. 45080, Zapopan, Jalisco, Mexico
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dc.identifier.urlhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84893556601&partnerID=40&md5=8905f32f0549a0374daf8d381e395ceb
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