An enhanced neural MRAS sensorless technique based on minor-component-analysis for induction motor drives

This paper proposes a new sensorless technique for induction motor drives based on a hybrid MRAS-neural technique. This MRAS method is an improvement of an already developed neural MRAS based sensorless method. In this paper the open-loop integration in the reference model is performed by an improved adaptive neural integrator. The adaptive model is based on a more accurate discrete current model and is trained on-line by a generalized least squares technique, the MCA EXIN + neuron, in which a parameterized learning algorithm is used. As a consequence, the speed estimation presents an improved convergence with higher accuracy and shorter settling time, because of the better transient behaviour of the neuron. A test bench has been set up to verify the methodology experimentally and the results prove its goodness at very low speeds ( below 4rad/s) and in zero-speed operation.