Particle Swarm Based Approach of a Real-Time Discrete Neural Identifier for Linear Induction Motors

Abstract EN

This paper focusses on a discrete-time neural identifier applied to a linear induction motor (LIM) model, whose model is assumed to be unknown.

This neural identifier 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 a novel algorithm based on extended Kalman filter (EKF) and particle swarm optimization (PSO), using an online series-parallel configuration.

Real-time results are included in order to illustrate the applicability of the proposed scheme.