Sensorless vector-controlled induction motor drives: Boosting performance with Adaptive Neuro-Fuzzy Inference System integrated augmented Model Reference Adaptive System
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View abstract on PubMed
Summary
This summary is machine-generated.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.
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.
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