针对具有高斯噪声的分数阶混沌系统的具有有限时间终端滑模控制功能的递归神经网络

IF 1.6 4区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Indian Journal of Physics Pub Date : 2023-06-20 DOI:10.1007/s12648-023-02778-w

Zengyue Zhan, Xiaoshan Zhao, Ruilong Yang

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引用次数: 0

摘要

针对含有高斯白噪声的分数阶混沌系统，提出了一种基于递归神经网络（RNN）的新型有限时间终端滑模控制（TSMC）。同时，还有更精确的检测目标。首先，我们可以使状态变量的跟踪误差在有限时间内快速收敛为零。然后将该方案应用于分数阶 PMSM 系统，并通过数值仿真验证了控制方案的有效性。基于以上两点，后者的影响更大。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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

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.

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来源期刊

Indian Journal of Physics 物理-物理：综合

CiteScore

3.40

自引率

10.00%

发文量

275

审稿时长

3-8 weeks

期刊介绍： Indian Journal of Physics is a monthly research journal in English published by the Indian Association for the Cultivation of Sciences in collaboration with the Indian Physical Society. The journal publishes refereed papers covering current research in Physics in the following category: Astrophysics, Atmospheric and Space physics; Atomic & Molecular Physics; Biophysics; Condensed Matter & Materials Physics; General & Interdisciplinary Physics; Nonlinear dynamics & Complex Systems; Nuclear Physics; Optics and Spectroscopy; Particle Physics; Plasma Physics; Relativity & Cosmology; Statistical Physics.