Mathematical Modelling and Performance Assessment of Neural Network-Based Adaptive Law of Model Reference Adaptive System Estimator at Zero and Very Low Speeds in the Regenerating Mode

Mohamed S. Zaky (), Kotb B. Tawfiq () and Mohamed K. Metwaly
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Mohamed S. Zaky: Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia
Kotb B. Tawfiq: Department of Electromechanical, Systems and Metal Engineering, Ghent University, 9000 Ghent, Belgium
Mohamed K. Metwaly: Department of Electrical Engineering, College of Engineering, Taif University, Taif 21974, Saudi Arabia

Mathematics, 2025, vol. 13, issue 11, 1-18

Abstract: Precise speed estimation of sensorless induction motor (SIM) drives remains a significant challenge, particularly at zero and very low speeds. This paper proposes a mathematically modeled and enhanced stator current-based Model Reference Adaptive System (MRAS) estimator integrated with correction terms using rotor flux dynamics to continually update the value of the estimated speed to the correct value. The MRAS observer uses the stator current in the adjustable IM model instead of the rotor flux or the back emf to eliminate the effect of pure integration of the rotor flux, the parameters’ deviation, and measurement errors of stator voltages and currents on speed observation. It depends on the observed stator current, the current estimate error, and rotor flux estimation correction terms. A neural network (NN) for the adaptive law of the MRAS observer is proposed to enhance the accuracy of the suggested approach. Simulation results examine the developed method. A laboratory prototype based on DSP-DS1103 was also built, and the experimental results are presented. The SIM drive is examined at zero and very low speeds in motoring and regenerating modes. It exhibits good dynamic performance and low-speed estimation error compared to the conventional MRAS.

Keywords: sensorless; induction motor; neural network; MRAS observer; stator current-based MRAS; regenerative mode; low-speed estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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