MRAS speed observer for sensorless adaptive intelligent backstepping controller of induction machines

Abstract

In this paper, a sensorless adaptive neuro-fuzzy backstepping control scheme is developed for induction machines with unknown model, uncertain load-torque and nonlinear friction where the speed is obtained using the model reference adaptive system (MRAS). Neurofuzzy systems are used to online approximate the uncertain nonlinearities and an adaptive backstepping technique is employed to systematically construct the control law. The MRAS observer is based on two different models, one that is speed independent (reference model) and another that is speed dependent (adjustable model). The speed is estimated via the outputs of the two models. The proposed sensorless controller guarantees the tracking errors converge to a small neighbourhood of the origin and the boundedness of all closed-loop signals what demonstrate the effectiveness of the proposed approach.