Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record. (1st May 2018)

• Record Type: Journal Article
• Authors: Relan, Rishi; Tiels, Koen; Marconato, Anna; Dreesen, Philippe; Schoukens, Johan
• Is Part Of: Mechanical systems and signal processing. Volume 104(2018)
• Page Start: 929
• Page End: 943
• Publication Date: 2018-05-01
• Subjects: Nonlinear system identification -- Nonlinear state space model -- Short-data record -- Soft and hard nonlinearities -- Multivariate polynomials -- Tensor decomposition; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2017.09.015
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A flexible nonlinear state space model structure for capturing both hard and weakly nonlinear behavior of the system. Ease of initialization of model structure for optimisation using two different schemes. Explicit estimation of the unknown initial values of the states. Ease to obtain a parsimonious representation of model structure to deal with short data records. 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.