Neural sensorless control of linear induction motors by a full-order Luenberger observer considering the end-effects

This paper proposes a neural based full-order Luenberger Adaptive speed observer for sensorless linear induction motor (LIM) drives, where the linear speed is estimated on the basis of the linear neural network: TLS EXIN neuron. With this reference, a novel state space-vector representation of the LIM has been deduced, taking into consideration the so-called end effects. Starting from this standpoint, the state equation of the LIM has been discretized and rearranged in a matrix form to be solved by a least-square technique. The TLS EXIN neuron has been used to compute on-line, in recursive form, the machine linear speed since it is the only neural network able to solve on-line in a recursive form a total least-squares problem. The proposed TLS full-order Luenberger Adaptive speed observer has been tested experimentally on suitably developed test setup. © 2012 IEEE.