Real-time discrete recurrent high order neural observer for induction motors

dc.contributorAlanis, A.Y., CUCEI, Universidad de Guadalajara, Av. Revolucion 1500, Col. Olimpica, C.P. 44430, Guadalajara, Jalisco, Mexico; Sanchez, E.N., CINVESTAV, Unidad Guadalajara, Plaza La Luna, Apartado Postal 31-438, Guadalajara, Jalisco, C.P. 45091, Mexico; Loukianov, A.G., CINVESTAV, Unidad Guadalajara, Plaza La Luna, Apartado Postal 31-438, Guadalajara, Jalisco, C.P. 45091, Mexico
dc.creatorAlanis, A.Y.
dc.creatorSanchez, E.N.
dc.creatorLoukianov, A.G.
dc.date.accessioned2015-09-15T18:47:58Z
dc.date.accessioned2022-11-02T14:17:15Z
dc.date.available2015-09-15T18:47:58Z
dc.date.available2022-11-02T14:17:15Z
dc.date.created2015-09-15T18:47:58Z
dc.date.issued2008
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-56349150548&partnerID=40&md5=b08d2cda92052ff4a3dde003791e0478
dc.identifierhttp://hdl.handle.net/20.500.12104/44089
dc.identifier10.1109/IJCNN.2008.4633923
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4994849
dc.description.abstractA nonlinear discrete-time neural observer for the state estimation of a discrete-time induction motor model, in presence of external and internal uncertainties is presented. The observer is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. This observer estimates the state of the unknown discrete-time nonlinear system, using a parallel configuration. The paper also includes the stability proof on the basis of the Lyapunov approach. To illustrate the applicability real-time results are included. � 2008 IEEE.
dc.relationScopus
dc.relationProceedings of the International Joint Conference on Neural Networks
dc.relation1012
dc.relation1018
dc.titleReal-time discrete recurrent high order neural observer for induction motors
dc.typeConference Paper


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