BIBO Stability for NOE Model Structure Using HL CPWL Functions

LILIANA RAQUEL CASTRO; JOSE LUIS FIGUEROA; OSVALDO ENRIQUE AGAMENNONI

Innsbruck

Congreso; IASTED International Conference Modelling, Identification and Control; 2005

IASTED

In this paper we propose a nonlinear output error (NOE) model structure for black-box identification of nonlinear dynamic systems. This model structure allows the implementation of an identification algorithm in which the degrees of freedom of the model can be easily increased or decreased during the identification process. This is done using High Level Canonical Piecewise Linear (HLCPWL) functions with an increasing (decreasing) grid division. Therefore, the algorithm can start using a linear estimation of the model. The parameters of the HLCPWL functions are updated using a simple algorithm based on an improved steepest descent method with an adaptive learn-ingrate. Furthermore, an algorithm to evaluate straight-forwardly the parameters of the HLCPWL approximation when going from a coarse division of the simplicial partition of the domain to a finer one, is given. A simple condition to update the parameters preserving BIBO stability is also derived. In order to show the behavior of the proposed algorithm, the identification of a well known dynam-ical system example from the literature is present.