.png){kind=icon}
.png){kind=icon}
Return
Discrete differentiators based on sliding modes
.png){kind=icon}
PRE
AI
DOI：10.1016/j.automatica.2019.108633.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Sliding-mode-based differentiation of the input f(t) yields exact estimations of the derivatives f, ..., f((n)), provided an upper bound L(t) of vertical bar f((n+1))(t)vertical bar is available in real-time. In practice it involves discrete sampling and numerical integration of the internal variables between the measurements. Accuracy asymptotics of different discretization schemes are calculated for discrete noisy sampling, whereas sampling and integration steps are independently variable or constant. Proposed discrete differentiators restore the optimal accuracy asymptotics of their continuous-time counterparts. Event-triggered sampling is considered. Extensive numeric experiments are presented and analyzed. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Differentiators
Sliding mode
Sampled signals
Digital filters
Accuracy
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