.png){kind=icon}
.png){kind=icon}
Return
Robust exact differentiators with predefined convergence time
.png){kind=icon}
OA
AI
DOI：10.1016/j.automatica.2021.109858.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The problem of exactly differentiating a signal with bounded second derivative is considered. A class of differentiators is proposed, which converge to the derivative of such a signal within a fixed, i.e., a finite and uniformly bounded convergence time. A tuning procedure is derived that allows to assign an arbitrary, predefined upper bound for this convergence time. It is furthermore shown that this bound can be made arbitrarily tight by appropriate tuning. The usefulness of the procedure is demonstrated by applying it to the well-known uniform robust exact differentiator, which is included in the considered class of differentiators as a special case. (C) 2021 The Author(s). Published by Elsevier Ltd.
Keywords:
Sliding modes
Super-twisting algorithm
Finite-time convergence
Fixed-time convergence
Disturbance rejection
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W
View more organizations