Simulation of MRAS-based speed sensorless estimation of induction motor drives using MATLAB/SIMULINK

Abstract

Model Reference Adaptive System (MRAS) based techniques are one of the best methods to estimate the rotor speed due to its performance and straightforward stability approach. These techniques use two different models (the reference model and the adjustable model) which have made the speed estimation a reliable scheme especially when the motor parameters are poorly known or having large variations. The scheme uses the error vector from the comparison of both models as the feedback for speed estimation. Depending on the type of tuning signal driving the adaptation mechanism, there could be a number of schemes available such as rotor flux based MRAS, back e.m.f based MRAS, reactive power based MRAS and artificial neural network based MRAS. All these schemes have their own trends and tradeoffs. In this paper, the performance of the rotor flux based MRAS (RF-MRAS) and back e.m.f based MRAS (BEMF-MRAS) for estimating the rotor speed was studied. Both schemes use the stator equation and rotor equation as the reference model and the adjustable model respectively. The output error from both models is tuned using a PI controller yielding the estimated rotor speed. The dynamic response of the RF-MRAS and BEMF-MRAS sensorless speed estimation is examined in order to evaluate the performance of each scheme.