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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Tuan V. Pham
Vinh University of Technical Education
Control & Systems Engineering Electrical & Electronics Engineering

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A neural learning algorithm for online rotor resistance estimation in sensorless induction motor drive systems Tuan V. Pham; Nguyen H. Thai
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i2.pp920-932

Abstract

This research proposes an advanced artificial neural network (ANN) framework optimized for the dynamic, real-time identification of rotor resistance (Rr) in sensorless induction motor (IM) drive systems. The proposed architecture introduces a self-tuning momentum factor within the neural learning update rule, which is adaptively modulated at each sampling interval. This modulation is governed by a Mamdani-based fuzzy inference system to ensure accelerated convergence and enhanced stability of the estimation process. Concurrently, the motor's angular velocity is estimated through a parallel ANN observer. Reliable identification of the time-varying rotor resistance is pivotal for compensating parametric sensitivity in flux observers, thereby optimizing the drive's control fidelity under varying thermal and load conditions. Comprehensive simulation and hardware-in-the-loop experimental results confirm that the proposed estimator tracks the actual Rr with high precision, maintaining steady-state errors within a 5% threshold.
Co-Authors Nguyen H. Thai