Differentiator for Noisy Sampled Signals with Best Worst-Case Accuracy

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

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.

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