Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record

Rishi Relan, Koen Tiels, Anna Marconato, Joannes Schoukens

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper, a methodology to identify a parsimonious discrete-time nonlinear state space model (NLSS) for the nonlinear dynamical system with relatively short data record is proposed. The capability of the NLSS model structure is demonstrated by introducing two different initialisation schemes, one of them using multivariate polynomials. In addition, a method using first-order information of the multivariate polynomials and tensor decomposition is employed to obtain the parsimonious decoupled representation of the set of multivariate real polynomials estimated during the identification of NLSS model. Finally, the experimental verification of the model structure is done on the cascaded water-benchmark identification problem.
Original languageEnglish
Pages (from-to)929-943
Number of pages15
JournalMechanical Systems and Signal Processing
Volume104
Issue number5
Early online date2017
DOIs
Publication statusPublished - May 2018

Keywords

  • Multivariate polynomials
  • Nonlinear state space model
  • Nonlinear system identification
  • Short-data record
  • Soft and hard nonlinearities
  • Tensor decomposition

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