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Open Access | Just Accepted

Optimization of Speed Control and Reduction of Torque Ripple in Switched Reluctance Motors Using Metaheuristic Algorithms Based PID and FOPID Controllers at the Edge

Mostafa Jabari1( )Amin Rad2

1 Electrical Engineering Faculty, Sahand University of Technology, Tabriz, Iran

2 Electrical Engineering Faculty, Kamal University, Urmia, Iran

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Abstract

This paper demonstrates the application of optimization techniques, namely the Dung Beetle Optimizer (DBO) and the Ant-Lion Optimizer (ALO), to enhance the performance of cascaded Proportional Integral Derivative (PID) and Fractional Order PID (FOPID) controllers at the edge of an industrial network for Switched Reluctance Motor (SRM) speed control and torque ripple reduction. These techniques present notable advantages in terms of faster convergence and reduced computational complexity compared to existing optimization methods. Our research employs PID and FOPID controllers to regulate the speed and torque of the SRM, with a comparative analysis of other optimization approaches. In the domain of SRM control, we highlight the significance of the hysteresis band block in mitigating sudden state transitions, especially crucial for ensuring stable operation in the presence of noisy or slightly variable input signals requiring precise control. The results underscore the superior performance of the proposed optimization strategies, particularly showcasing the DBO-based cascaded PID and FOPID controllers, which exhibit reduced torque and current ripples along with improved speed response. Our investigation encompasses diverse loading conditions and is substantiated through time-domain simulations performed using MATLAB/SIMULINK.

Tsinghua Science and Technology
Cite this article:
Jabari M, Rad A. Optimization of Speed Control and Reduction of Torque Ripple in Switched Reluctance Motors Using Metaheuristic Algorithms Based PID and FOPID Controllers at the Edge. Tsinghua Science and Technology, 2024, https://doi.org/10.26599/TST.2024.9010021

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Received: 17 October 2023
Revised: 17 December 2023
Accepted: 15 January 2024
Available online: 19 June 2024

© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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