Structure detection of Wiener-Hammerstein systems with process noise

TL;DR

This paper introduces a novel method to identify the location of process noise in Wiener-Hammerstein systems, improving nonlinear system modeling accuracy by analyzing output disturbances influenced by input signals.

Contribution

The work presents a new protocol for localizing process noise within block-oriented models using a periodic, nonstationary input, and also detects specific types of static nonlinearities.

Findings

Effective localization of process noise demonstrated on simulated data.

Method successfully applied to real-life benchmark system.

Potential to identify static nonlinearity types like dead-zone or saturation.

Abstract

Identification of nonlinear block-oriented models has been extensively studied. The presence of the process noise, more precisely its location in the block-oriented model influences essentially the development of a consistent identification algorithm. The present work is proposed with the aim to localize the process noise in the block-oriented model for accurate nonlinear modeling. To this end, the response of a Wiener-Hammerstein system is theoretically analyzed, the disturbance component in the output, caused by the process noise preceding the static nonlinearity, is shown to be dependent on the input signal. Inspired by such theoretical observation, a simple and new protocol is developed to determine the location of the process noise with respect to the static nonlinearity by using an input signal that is periodic, but nonstationary within one period. In addition, the proposed technique is promising to detect the type of certain static nonlinearity (e.g., dead-zone, saturation). Finally, it is validated on a simulated example and a real-life benchmark.