A methodology for identification of NARMAX models applied to diesel engines

Gianluca Zito (1) , Ioan Doré Landau (1)

1 LAG - Laboratoire d'automatique de Grenoble

In this paper a nonlinear system identification methodology based on a polynomial NARMAX model representation is considered. Algorithms for structure selection and parameter estimation are presented and evaluated. The goal of the procedure is to provide a nonlinear model characterized by a low complexity and that can be efficiently used in industrial applications. The methodology is illustrated by means of an automotive case study, namely a variable geometry turbocharged diesel engine. The nonlinear model representing the relation between the variable geometry turbine command and the intake manifold air pressure is identified from data and validated.

Mots clés: nonlinear models; system identification; identification algorithms; automotive

Domaines: Automatique

Gianluca Zito, Ioan Doré Landau. A methodology for identification of NARMAX models applied to diesel engines. IFAC World Congress 2005, Jul 2005, Prague, Czech Republic. ⟨hal-00384859⟩