A new intelligent adaptation mechanism of MRAS based on a genetic algorithm applied to speed sensorless direct torque control for induction motor

Efficient sensorless approaches for controlled electrical motors reside in their simplicity and robustness, even with a wide range of operating speeds such as the model reference adaptive system. However, the linear tuning method of the PI regulator in the adaptation mechanism of the mentioned observer degrades the motor performance in the presence of external disturbances and parameter variations. This paper proposes an optimized genetic algorithm to automatically tune the PI regulator in the model reference adaptive system based on several execution sequences. A comparative research study between the conventional model reference adaptive system and the same observer based on a genetic algorithm was performed using the MATLAB/SIMULINK simulation platform. The analysis of this study was executed under various operating conditions. The results prove that this strategy can enhance speed and tracking performance while guaranteeing excellent behavior of overshoot and rejection time.