Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6954730 | Mechanical Systems and Signal Processing | 2018 | 15 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Rishi Relan, Koen Tiels, Anna Marconato, Philippe Dreesen, Johan Schoukens,