Polynomial State-Space Model Decoupling for the Identification of Hysteretic Systems. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Esfahani, Alireza Fakhrizadeh; Dreesen, Philippe; Tiels, Koen; Noël, Jean-Philippe; Schoukens, Johan
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 458
• Page End: 463
• Publication Date: 2017-07
• Subjects: Polynomial Nonlinear State-Space -- Hysteretic System -- Bouc-Wen -- Tensor Decomposition -- Canonical Polyadic Decomposition -- Decoupling Multivariate Polynomials; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.082
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Hysteresis is a nonlinear effect that shows up in a wide variety of engineering and scientific fields. The identification of hysteretic systems from input-output data is an important but challenging question, which has been studied by using both tailored parametric white-box identification methods as by using black-box identification methods. The white-box modeling approach is by far the most common in identifying hysteretic systems, and has the advantage of resulting into an interpretable model, but it requires to be adjusted to a specific hysteresis model. A black-box approach can be used more universally, but results in models containing many parameters that cannot easily be interpreted. In the current paper, we propose a two-step identification procedure that combines the best of the two approaches. We employ the Bouc-Wen hysteretic model to generate data that is used for identification. The system is identified using a black-box polynomial nonlinear state-space identification procedure. We reduce the number of parameters in this model by applying a polynomial decoupling method that results in a more parsimonious representation. We compare the full black-box model with the decoupled model and show that the proposed method results in a comparable performance, while significantly reducing the number of parameters.