Title :
Discrete-time reduced order neural observer for Linear Induction Motors
Author :
Alanis, Alma Y. ; Sanchez, Edgar N. ; Hernandez-Gonzalez, Miguel ; Ricalde, Luis J.
Author_Institution :
CUCEI, Univ. de Guadalajara, Guadalajara, Mexico
Abstract :
This paper focusses on a discrete-time reduced order neural observer applied to a Linear Induction Motor (LIM) model, whose model is assumed to be unknown. This neural observer is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm, using a parallel configuration. Simulation results are included in order to illustrate the applicability of the proposed scheme.
Keywords :
Kalman filters; linear induction motors; observers; power engineering computing; recurrent neural nets; EKF-based algorithm; LIM model; RHONN; discrete-time recurrent high-order neural network; discrete-time reduced order neural observer; extended Kalman filter; linear induction motors; parallel configuration; Artificial neural networks; Covariance matrix; Induction motors; Kalman filters; Mathematical model; Nonlinear systems; Observers; Discrete-time nonlinear systems; Kalman filtering learning; Linear Induction Motor; Mass public electric transportation; Recurrent high order neural networks;
Conference_Titel :
Computational Intelligence Applications In Smart Grid (CIASG), 2011 IEEE Symposium on
Conference_Location :
Paris
Print_ISBN :
978-1-4244-9893-2
DOI :
10.1109/CIASG.2011.5953330
