Tracking Differentiators for Both the Real Time Signals and the Time Delayed Signals

被引:0
|
作者
Zhang, Xiaoqin [1 ]
Lang, Pei-Hua [2 ]
机构
[1] Shanxi Univ Finance & Econ, Sch Stat, Taiyuan 030006, Shanxi, Peoples R China
[2] Shanxi Univ, Sch Math Sci, Taiyuan, Shanxi, Peoples R China
来源
IEEE ACCESS | 2024年 / 12卷
基金
中国国家自然科学基金;
关键词
Noise; Convolutional neural networks; Real-time systems; Delay effects; Control theory; Tuning; Tracking; High-gain; mollifier function; tracking differentiator; weighted moving average; CONVERGENCE;
D O I
10.1109/ACCESS.2024.3417709
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The differential tracking for a given signal is a well-known and challenging problem in control theory and practice. In this note, we introduce a novel method to design the differentiators. The proposed differentiator, in contrast to existing differentiators that take the form of a dynamical system, is represented through convolutions. This idea is primarily inspired by the mollifier technique, which is well-known in the theory of partial differential equations. Both the weighted moving average technique and the mollifier technique are used in the differentiator's design. By the proper choice of the kernel function, we can obtain the derivative of the given signal by integrating rather than differentiating the signal itself directly. As a result, the proposed tracking differentiators can be robust to the high-frequency signals. Although our approach is simple, it is very effective, both for the real time signals and the time delayed signals.
引用
收藏
页码:113910 / 113917
页数:8
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