Journal of Applied Science and Engineering

Published by Tamkang University Press


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Mahmoud M. Adel1, Amr A. Saleh1This email address is being protected from spambots. You need JavaScript enabled to view it., Mohamed A. Hassan1,2, Ralph Kennel3, and Ahmed Farhan1

1Electrical Engineering Department, Faculty of Engineering, Fayoum University, 63514, Fayoum, Egypt

2Electrical Engineering Department, College of Engineering, King Faisal University, 31982, Al Ahsa, Saudi Arabia

3Chair of High-Power Converter System, Technical University of Munich, 80333, Munich, Germany



Received: October 30, 2023
Accepted: January 3, 2024
Publication Date: March 1, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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In this study, a simplified accurate technique for sensorless direct speed predictive control (DSPC) for the permanent magnet synchronous motor (PMSM) based on the model reference adaptive system (MRAS) is presented. The suggested (DSPC) approach utilizes an innovative method that uses all electrical and mechanical variables in a single control law to determine the best switching vector for the inverter during the following sampling interval. The proposed cost function is simple without weighting factors due to the employment of a sliding term containing the speed/current tracking. As a result, the proposed technique eliminates the PI controllers compared to the conventional vector control approach, getting rid of the drawbacks of the common vector control’s cascaded control structure (speed and current loops). Model reference adaptive system is employed to estimate the PMSM speed/position. Particle swarm optimization (PSO) technique is employed to tune the MRAS PI controller gain and integration parameters to get an accurate speed/position estimation. A fair comparison between the suggested DSPC and the conventional current predictive control (CPC), which employs a PI controller in the outer speed loop, is provided to clarify the aspects of the proposed method. To ensure the robustness of the proposed approach, the response for different dynamic operating points is examined using MATLAB M-file/Simulink simulation. The simulation results illustrate distinct response of the DSPC providing good settling time, rise time and steady state error and accepted overshoot with a satisfied torque/current ripples. Also, the results show the excellent tracking of PMSM motor speed/position using MRAS technique. According to simulation comparison results between the suggested DSPC and conventional CPC, the proposed DSPC exhibits much better transient and steady state performance.

Keywords: Permanent magnet synchronous motor; Model predictive control; Model reference adaptive system; Sensorless control; Particle swarm optimization

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