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Sensorless vector-controlled induction motor drives: Boosting performance with Adaptive Neuro-Fuzzy Inference System

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Summary

This study integrates an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller with a Model Reference Adaptive System (MRAS) for enhanced speed control in sensorless Induction Motor (IM) drives. The ANFIS-MRAS scheme improves dynamic performance and robustness, especially at low speeds.

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • Sensorless Induction Motor (IM) drives require robust speed control, particularly at zero and very low speeds.
  • Model Reference Adaptive Systems (MRAS) offer effectiveness but can be sensitive to parameter uncertainties and load variations.
  • Existing control methods face challenges in maintaining stability and dynamic performance under varying operating conditions.

Purpose of the Study:

  • To enhance the resilience and dynamic performance of sensorless vector-controlled IM drives.
  • To improve the speed tracking accuracy and operational smoothness of IM drives.
  • To mitigate the impact of parameter uncertainties and external disturbances on the control system.

Main Methods:

  • Integration of an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller with a Model Reference Adaptive System (MRAS).
  • Utilizing ANFIS to adaptively adjust controller parameters based on speed estimation errors.
  • Implementing a sensorless vector control strategy for Induction Motor (IM) drives.

Main Results:

  • The ANFIS-enhanced MRAS demonstrated superior dynamic performance and robustness compared to existing systems.
  • Improved reference speed tracking and smoother drive operation were achieved.
  • Reduced sensitivity to parameter variations, such as motor parameters and load torque, was observed.

Conclusions:

  • The proposed ANFIS-MRAS scheme is an effective solution for precise speed control in sensorless IM drives.
  • The integration significantly enhances system stability and reliability, especially under challenging conditions.
  • This approach is well-suited for applications demanding high precision and dependability in IM speed control.