A nonlinear state-space approach to hysteresis identification

TL;DR

This paper introduces a flexible black-box nonlinear state-space method for hysteresis identification, avoiding specific model assumptions and validated through synthetic data, enhancing adaptability in practical applications.

Contribution

It presents a novel nonlinear state-space framework for hysteresis identification that is more flexible and parsimonious than traditional white-box models.

Findings

Effective hysteresis modeling with synthetic data

Model validation under broadband and sine conditions

Discussion on model order and polynomial degree selection

Abstract

Most studies tackling hysteresis identification in the technical literature follow white-box approaches, i.e. they rely on the assumption that measured data obey a specific hysteretic model. Such an assumption may be a hard requirement to handle in real applications, since hysteresis is a highly individualistic nonlinear behaviour. The present paper adopts a black-box approach based on nonlinear state-space models to identify hysteresis dynamics. This approach is shown to provide a general framework to hysteresis identification, featuring flexibility and parsimony of representation. Nonlinear model terms are constructed as a multivariate polynomial in the state variables, and parameter estimation is performed by minimising weighted least-squares cost functions. Technical issues, including the selection of the model order and the polynomial degree, are discussed, and model validation is achieved in both broadband and sine conditions. The study is carried out numerically by exploiting synthetic data generated via the Bouc-Wen equations.