Differentiator for Noisy Sampled Signals with Best Worst-Case Accuracy

Hernan Haimovich*, Richard Seeber, Rodrigo Aldana-López, David Gómez-Gutiérrez

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikel

Abstract

This letter proposes a differentiator for sampled signals with bounded noise and bounded second derivative. It is based on a linear program derived from the available sample information and requires no further tuning beyond the noise and derivative bounds. A tight bound on the worst-case accuracy, i.e., the worst-case differentiation error, is derived, which is the best among all causal differentiators and is moreover shown to be obtained after a fixed number of sampling steps. Comparisons with the accuracy of existing high-gain and sliding-mode differentiators illustrate the obtained results.
Originalspracheenglisch
Aufsatznummer9448332
Seiten (von - bis)938-943
Seitenumfang6
FachzeitschriftIEEE Control Systems Letters
Jahrgang6
DOIs
PublikationsstatusVeröffentlicht - 2022

ASJC Scopus subject areas

  • !!Control and Optimization
  • !!Control and Systems Engineering

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