Recurrent neural networks with finite-time terminal sliding mode control for the fractional-order chaotic system with Gaussian noise

被引：3
|
作者：
Zhan, Zengyue [1 ]
Zhao, Xiaoshan [1 ]
Yang, Ruilong [1 ]
机构：
[1] Tianjin Univ Technol & Educ, Coll Sci, Tianjin, Peoples R China
来源：
INDIAN JOURNAL OF PHYSICS | 2024年 / 98卷 / 01期
关键词：
Recurrent neural networks; Gaussian noise; Fractional calculus; Terminal sliding mode control; SYNCHRONIZATION; PARAMETERS;
D O I：
10.1007/s12648-023-02778-w
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
A new finite-time terminal sliding mode control (TSMC) based on recurrent neural networks (RNN) is proposed aiming at fractional-order chaotic systems containing Gaussian white noise. At the same time, there are more accurate detection targets. Firstly, we can make tracking errors of state variables converge to zero quickly in finite time. Then the scheme is applied to the fractional-order PMSM system, and the effectiveness of the control scheme is verified by numerical simulation. Based on the above two points, the latter has more influence.
引用
收藏
页码：291 / 300
页数：10
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