Differentiator for Noisy Sampled Signals with Best Worst-Case Accuracy
Optimization and Control
2021-06-11 v1 Systems and Control
Systems and Control
Abstract
This paper 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.
Cite
@article{arxiv.2106.05320,
title = {Differentiator for Noisy Sampled Signals with Best Worst-Case Accuracy},
author = {Hernan Haimovich and Richard Seeber and Rodrigo Aldana-López and David Gómez-Gutiérrez},
journal= {arXiv preprint arXiv:2106.05320},
year = {2021}
}
Comments
Please cite the publisher's version. For the publisher's version and full citation details see: https://doi.org/10.1109/LCSYS.2021.3087542