Robust IM control with MRAS-based speed and parameters estimation with ANN using exponential reaching law

As known, the main cause of the degradation in indirect rotor field oriented induction motor (IM) control (IRFOC) is the time-varying machine parameters, especially the rotor-time constant (T_r) and stator resistance (R_s), more pertinently, in cases of proportional-integral control with speed observation. In this work, a new exponential reaching law (ERL) based sliding mode control (SMC) is introduced to improve significantly the performances when compared to the conventional SMC which are well known susceptible to the annoying chattering phenomenon. So, the elimination of the chattering is achieved while simplicity and high performance speed tracking are maintained. In addition, an artificial neural network (ANN) technique is used to achieve an accurate on-line conjoint estimation of the most influent parameters on IRFOC. This technique is integrated in the adaptation mechanism of the model reference adaptive system (MRAS) in order to obtain adaptive sensorless scheme. The merits of the proposed method are demonstrated experimentally through a test-rig realized via the dSPACE DS1104 card in various operating conditions.