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Neural modelling and control of a Diesel engine with pollution constraints

Authors
  • Ouladsine, Mustapha1
  • Bloch, Gérard2
  • Dovifaaz, Xavier2
  • 1 LSIS, Domaine Universitaire de Saint-Jérôme (UMR CNRS 6168), Avenue de l’Escadrille Normandie Niemen, Marseille Cedex 20, 13397, France , Marseille Cedex 20
  • 2 CRAN-ESSTIN, Centre de Recherche en Automatique de Nancy (CRAN, UMR CNRS 7039), 2 rue Jean Lamour, Vandoeuvre, 54500, France , Vandoeuvre
Type
Published Article
Journal
Journal of Intelligent & Robotic Systems
Publisher
Springer-Verlag
Publication Date
Jan 01, 2005
Volume
41
Issue
2-3
Pages
157–171
Identifiers
DOI: 10.1007/s10846-005-3806-y
Source
Springer Nature
Keywords
License
Yellow

Abstract

The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnected neural submodels, each of them constituting a nonlinear multi-input single-output error model. The structural identification and the parameter estimation from data gathered on a real engine are described. The neural direct model is then used to determine a neural controller of the engine, in a specialized training scheme minimising a multivariable criterion. Simulations show the effect of the pollution constraint weighting on a trajectory tracking of the engine speed. Neural networks, which are flexible and parsimonious nonlinear black-box models, with universal approximation capabilities, can accurately describe or control complex nonlinear systems, with little a priori theoretical knowledge. The presented work extends optimal neuro-control to the multivariable case and shows the flexibility of neural optimisers. Considering the preliminary results, it appears that neural networks can be used as embedded models for engine control, to satisfy the more and more restricting pollutant emission legislation. Particularly, they are able to model nonlinear dynamics and outperform during transients the control schemes based on static mappings.

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