Torque Ripple Reduction Of Switched Reluctance Motor Based On Neural Network Sliding Mode Parameter Online Learning

被引：0
|
作者：
Jing, Benqin [1 ,2 ]
Dangl, Xuanju [1 ]
Liu, Zheng [2 ]
Wang, Jianqi [2 ]
Jiang, Yanjun [2 ]
机构：
[1] Guilin Univ Elect Technol, Sch Elect Engn & Automat, Guilin, Peoples R China
[2] Guilin Univ Aerosp Technol, Sch Elect Informat & Automat, Guilin, Peoples R China
来源：
JOURNAL OF APPLIED SCIENCE AND ENGINEERING | 2023年 / 27卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Neural network sliding mode; Switched reluctance motor; Parameter online learning; Torque ripple; CONTROLLER;
D O I：
10.6180/jase.20240627(6).0013
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
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.
引用
收藏
页码：2667 / 2673
页数：7
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