Differentiator for Noisy Sampled Signals With Best Worst-Case Accuracy

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...

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Veröffentlicht in:	IEEE control systems letters 2022, Vol.6, p.938-943
Hauptverfasser:	Haimovich, Hernan, Seeber, Richard, Aldana-Lopez, Rodrigo, Gomez-Gutierrez, David
Format:	Artikel
Sprache:	eng
Schlagworte:	Convergence Differentiation Doppler effect estimation Noise measurement Observers Optimization Time measurement Tuning
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creator	Haimovich, Hernan Seeber, Richard Aldana-Lopez, Rodrigo Gomez-Gutierrez, David
description	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.
doi_str_mv	10.1109/LCSYS.2021.3087542
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subjects	Convergence Differentiation Doppler effect estimation Noise measurement Observers Optimization Time measurement Tuning
title	Differentiator for Noisy Sampled Signals With Best Worst-Case Accuracy
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