Design of a neural virtual sensor for the air and charging system in a Diesel engine

The main objective of this work is to design a virtual sensor capable of estimating variables that are unmeasurable on-line in the air and charging subsystem of a Diesel engine. In order to achieve this objective, a data-driven approach is pursued. In particular, we show that combining high-gain observers and feed-forward neural networks, it is possible to design an observer for the air and charging system of a Diesel engine on the basis of data acquired via a test bench. The performance of this observer is evaluated in a real experimental setting.