A nonlinear state-space approach to hysteresis identification
For engineers dealing with real-world hysteresis, this black-box method removes the need for a priori model assumptions, offering a more general and practical identification framework.
The paper proposes a black-box nonlinear state-space approach for hysteresis identification, demonstrating flexibility and parsimony without assuming a specific hysteretic model. Validation on synthetic Bouc-Wen data shows accurate identification in both broadband and sine conditions.
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.