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This paper proposes a neural network (NN) model reference adaptive system (MRAS) speed observer suited for linear induction motor (LIM) drives. The voltage and current flux models of the LIM in the stationary reference frame, taking into consideration the end effects, have been first deduced. Then, the induced part equations have been discretized and rearranged so as to be represented by a linear NN (ADALINE). On this basis, the transport layer security EXIN neuron has been used to compute online, in recursive form, the machine linear speed. The proposed NN MRAS observer has been tested experimentally on suitably developed test set-up. Its performance has been further compared to the classic MRAS and the sliding-mode MRAS speed observers developed for the rotating machines. Index Terms-Field-oriented control (FOC), linear induction motor (LIM), model reference adaptive systems (MRASs), neural networks (NNs), sensorless control.NOMENCLATURE u s = u sD + j u sQ Space vector of the inductor voltages in the inductor reference frame. i s = i sD + j i sQ Space vector of the stator currents in the inductor reference frame. ψ r = ψ rd + j ψ rq Space vector of the induced part fluxlinkages in the stator reference frame. ρ r Phase angle of the induced part flux linkage space vector with respect to the sD axis. L s Inductor inductance. L r Induced part inductance. L m Three-phase magnetizing inductance. L σ s Inductor leakage inductance. L σ r Induced part leakage inductance.

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This study proposes a state formulation of the space-vector dynamic model of the Synchronous Reluctance Motor (SynRM) considering both saturation and cross-saturation effects. The proposed model adopts the stator currents as state variables and has been theoretically developed in both the rotor and stator reference frames. The proposed magnetic model is based on a flux versus current approach and relies on the knowledge of 11 parameters. Starting from the definition of a suitable co-energy variation function, new flux versus current functions have been initially developed, based on the hyperbolic functions and, consequently, the static and dynamic inductance versus current functions have been deduced. The dynamic inductance functions have been derived so to fulfill the reciprocity conditions. This study presents also a technique for the estimation of the parameters of the proposed magnetic model, which is based on standstill tests without the need to lock the rotor. The identification process has been performed based on the minimization of a suitably defined error function including the difference between the measured and estimated stator fluxes. The proposed parameter estimation technique has been tested in both numerical simulation and experimentally on a suitably developed test setup , permitting the experimental validation of the proposed model.