  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.
