Neural based MRAS sensorless techniques for high performance linear induction motor drives

Abstract

This paper proposes a neural based MRAS (Model reference Adaptive System) speed observer suited for linear induction motors (LIM). Starting from the dynamical equation of the LIM in the synchronous reference frame in literature, the so-called voltage and current models of the LIM in the stationary reference frame, taking into consideration the end effects, have been deduced. Then, while the inductor equations have been used as reference model of the MRAS observer, the induced part equations have been discretized and rearranged so to be represented by a linear neural network (ADALINE). On this basis, the so called TLS EXIN neuron has been used to compute on-line, in recursive form, the machine linear speed. As machine under test, a complete dynamic model, based on the constructive elements of the LIM and taking into consideration the end effects by the definition of a proper air-gap function, has been adopted. The proposed MRAS observer has been tested in numerical simulation at high and low linear speeds of the inductor and has been further compared with both the classic MRAS observer taking into consideration the LIM's end effects and with the MRAS observer based on the RIM's equations, not considering the end effects.