论具有多重随机扰动的跟踪微分器的收敛性

IF 7.3 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Science China Information Sciences Pub Date : 2023-12-28 DOI:10.1007/s11432-022-3815-4

Zehao Wu, Huacheng Zhou, Baozhu Guo, Feiqi Deng

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引用次数: 0

摘要

本文首次研究了跟踪微分器在多种随机干扰下的收敛性、噪声容限和滤波性能。我们考虑了一种一般情况，即输入信号受到加性彩色噪声的干扰，跟踪微分器受到加性彩色噪声和白噪声的干扰。结果表明，跟踪微分器能在均方意义上跟踪输入信号及其广义导数。此外，当影响输入信号的随机噪声消失时，可以实现几乎确定的收敛。在此，我们进行了数值模拟来验证理论结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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On the convergence of tracking differentiator with multiple stochastic disturbances

This paper investigates the convergence, noise-tolerance, and filtering performance of a tracking differentiator in the presence of multiple stochastic disturbances for the first time. We consider a general case wherein the input signal is corrupted by additive colored noise, and the tracking differentiator is disturbed by additive colored noise and white noise. The tracking differentiator is shown to track the input signal and its generalized derivatives in the mean square sense. Further, the almost sure convergence can be achieved when the stochastic noise affecting the input signal is vanishing. Herein, numerical simulations are performed to validate the theoretical results.

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来源期刊

Science China Information Sciences COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

12.60

自引率

5.70%

发文量

224

审稿时长

8.3 months

期刊介绍： Science China Information Sciences is a dedicated journal that showcases high-quality, original research across various domains of information sciences. It encompasses Computer Science & Technologies, Control Science & Engineering, Information & Communication Engineering, Microelectronics & Solid-State Electronics, and Quantum Information, providing a platform for the dissemination of significant contributions in these fields.