Model Reference Adaptive System Based on Ultra-Local Model for Induction Motor Drives

Induction motors' longevity, ease of use, and relative affordability have contributed to their widespread adoption across various industries and application domains for their low cost and high efficiency. Model Reference Adaptive Systems (MRAS) have been widely used to obtain high performance at a reasonable cost. MRAS typically uses two models, the Reference and Adaptive, which rely heavily on motor characteristics. This reliance can impair performance in extreme environments, such as deep mines. This work describes a new Ultra Local Model-based MRAS (MRAS-ULM) that eliminates rotor parameter dependency in the adaptive model of standard MRAS. The performance and application of the MRAS-ULM Speed Sensorless Induction Motor drive are evaluated under various conditions. Experimental results confirm the efficacy and superiority of the suggested approach for induction motor applications in demanding conditions, such as HVAC systems.