Improved MRAS with stator and rotor resistance estimation for vector control of induction motors

Abstract

This research addresses the degradation of speed estimation accuracy in speed-sensorless induction motor drives that arises from temperature-dependent variations of stator and rotor resistances. These parameter changes significantly affect the Rotor Flux-Based Model Reference Adaptive System (RF-MRAS) observer, leading to reduced robustness and unstable operation under dynamic conditions. To mitigate this issue, an enhanced Field-Oriented Control (FOC) scheme is proposed, integrating an adaptive RF-MRAS observer with an online estimation mechanism for the stator resistance and a proportional inference approach for the rotor resistance. A complete induction motor model is formulated in the stationary α-β reference frame and subsequently transformed into the rotating d-q frame to facilitate the implementation of the vector control strategy. Simulation studies, conducted under both sudden step changes and gradual ramp variations of the motor resistances, verify that the proposed method significantly improves speed estimation accuracy and enhances overall system stability. Owing to its simple structure and low computational burden, the proposed approach is suitable for real-time embedded applications in modern sensorless motor drive systems.