Discrete-time reduced order neural observer for Linear Induction Motors

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.

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