Journal of Applied Science and Engineering

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Benqin Jing1,2, Xuanju Dang1This email address is being protected from spambots. You need JavaScript enabled to view it., Zheng Liu2, Jianqi Wang2, and Yanjun Jiang2

1School of Electronic and Automation, Guilin University of Electronic Technology, Guilin, China

2School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin , China

Received: December 16, 2022
Accepted: April 18, 2023
Publication Date: November 3, 2023

 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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High torque ripple limits the application area of the switched reluctance motor (SRM). To solve this problem, the sliding mode control algorithm is applied to the speed control in SRM. However, the uncertainty of motor parameters significantly impacts the electromagnetic torque of SRM. Therefore, a neural network sliding mode controller (NNSMC) based on parameter online learning is designed in this paper. The internal parameters of SRM are learned online through speed error, resulting in the combined control of the neural network and sliding mode. The Lyapunov stability method is used to prove the stability of the algorithm. The simulation results show that the proposed method can effectively learn the parameters of SRM, reduce torque ripple and improve the operational performance of the motor.

Keywords: Neural network sliding mode; Switched reluctance motor; Parameter online learning; Torque ripple

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