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Rotor‐flux‐based MRAS speed estimator for DTFC‐SVM of a speed sensorless induction motor drive using Type‐1 and Type‐2 fuzzy logic controllers over a wide speed range
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Rotor‐flux‐based MRAS speed estimator for DTFC‐SVM of a speed sensorless induction motor drive using Type‐1 and Type‐2 fuzzy logic controllers over a wide speed range
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Rotor‐flux‐based MRAS speed estimator for DTFC‐SVM of a speed sensorless induction motor drive using Type‐1 and Type‐2 fuzzy logic controllers over a wide speed range
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Journal Article
Rotor‐flux‐based MRAS speed estimator for DTFC‐SVM of a speed sensorless induction motor drive using Type‐1 and Type‐2 fuzzy logic controllers over a wide speed range
2016
Overview
Summary In this research study, Type‐1 and Type‐2 fuzzy logic controller‐based model reference adaptive system speed estimator for direct torque and flux control with space vector modulation of a speed sensorless induction motor drive is proposed to replace the conventional proportional‐integral controller. Initially, Type‐1 fuzzy logic controller is developed, which is used to achieve high performance sensorless drive in both transient as well as steady state conditions. However, the Type‐1 fuzzy sets are certain and unable to work effectively when higher degree of uncertainties in the system which can be caused by sudden change in speed or different load torque disturbances, process noise, etc. Therefore, a new Type‐2 fuzzy logic controller‐based adaptation scheme is proposed to better handle the higher degree of uncertainties along with enhancing the performance and also robust to various load torque and sudden change in speed conditions respectively. The detailed performances of various control schemes are carried out in MATLAB/SIMULINK with speed sensor and sensorless modes of operation when an induction motor drive is operating under different conditions, such as no‐load, load, sudden change in speed and low speed respectively. To validate the different control approaches, an experimental prototype is developed and adequate results are reported for its validation. Superior performance has been obtained using the Type‐2 fuzzy logic controller scheme in speed sensor and sensorless modes of operation compared with Type‐1 fuzzy logic controller and proportional‐integral controller based adaptation schemes respectively. Copyright © 2016 John Wiley & Sons, Ltd.
Publisher
John Wiley & Sons, Inc
