Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record
For control engineers, it offers a way to model weakly nonlinear systems with limited data, but the approach is incremental and domain-specific.
The paper proposes a methodology to identify a parsimonious discrete-time nonlinear state-space model for weakly nonlinear systems with short data records, validated on a cascaded water-benchmark problem.
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.