Adaptive differentiating filter: case study of PID feedback control

Fuente: arXiv
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Main Authors: Pavlov, Alexey, Ruderman, Michael
Format: Preprint
Published: 2026
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author Pavlov, Alexey
Ruderman, Michael
author_facet Pavlov, Alexey
Ruderman, Michael
contents This paper presents an adaptive causal discrete-time filter for derivative estimation, exemplified by its use in estimating relative velocity in a mechatronic application. The filter is based on a constrained least squares estimator with window adaptation. It demonstrates low sensitivity to low-amplitude measurement noise, while preserving a wide bandwidth for large-amplitude changes in the process signal. Favorable performance properties of the filter are discussed and demonstrated in a practical case study of PID feedback controller and compared experimentally to a standard linear low-pass filter-based differentiator and a robust sliding-mode based homogeneous differentiator.
format Preprint
id arxiv_https___arxiv_org_abs_2603_27615
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Adaptive differentiating filter: case study of PID feedback control
Pavlov, Alexey
Ruderman, Michael
Systems and Control
This paper presents an adaptive causal discrete-time filter for derivative estimation, exemplified by its use in estimating relative velocity in a mechatronic application. The filter is based on a constrained least squares estimator with window adaptation. It demonstrates low sensitivity to low-amplitude measurement noise, while preserving a wide bandwidth for large-amplitude changes in the process signal. Favorable performance properties of the filter are discussed and demonstrated in a practical case study of PID feedback controller and compared experimentally to a standard linear low-pass filter-based differentiator and a robust sliding-mode based homogeneous differentiator.
title Adaptive differentiating filter: case study of PID feedback control
topic Systems and Control
url https://arxiv.org/abs/2603.27615