Sensorless vector-controlled induction motor drives: Boosting performance with Adaptive Neuro-Fuzzy Inference System integrated augmented Model Reference Adaptive System.

The Model Reference Adaptive System (MRAS) is effective for speed control in sensorless Induction Motor (IM) drives, particularly at zero and very low speeds. This study enhances MRAS's resilience and dynamic performance by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller into sensorless vector-controlled IM drives. The research addresses challenges related to parameter uncertainties, load variations, and external disturbances through the combination of MRAS and ANFIS. The ANFIS controller enhances dynamic performance by adjusting its parameters based on the error between estimated and measured rotor speeds, which improves reference speed tracking and ensures smoother drive operation. This integration of ANFIS with MRAS reduces the sensitivity of the sensorless control system to parameter variations, such as changes in motor parameters or load torque, thereby enhancing system stability. The primary goal is to ma-intain stability and mitigate the impact of parameter variations on the sensorless control system. The proposed MRAS-ANFIS scheme was evaluated using MATLAB and compared with existing systems. Results show that the ANFIS-enhanced MRAS delivers superior dynamic performance and robustness, proving to be an effective solution for applications demanding precise speed control and high reliability. • integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) with MRAS enhances the dynamic performance and resilience of sensorless Induction Motor (IM) drives, particularly at zero and very low speeds.• The ANFIS controller adapts to parameter uncertainties, load variations, and disturbances, improving speed tracking and reducing sensitivity to motor parameter changes, thus enhancing system stability.• MATLAB simulations show that the ANFIS-enhanced MRAS outperforms existing systems, offering superior dynamic performance and robustness, making it ideal for precise speed control applications