Adaptive differentiating filter: case study of PID feedback control

TL;DR

This paper introduces an adaptive causal filter for derivative estimation in PID control, demonstrating improved noise sensitivity and bandwidth preservation through a case study and experimental comparisons.

Contribution

It proposes a novel adaptive differentiating filter based on constrained least squares with window adaptation, enhancing derivative estimation in control systems.

Findings

The filter shows low sensitivity to low-amplitude noise.

It maintains a wide bandwidth for large signal changes.

Experimental results outperform standard differentiators.

Abstract

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.