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Gasoline Direct Injection (GDI) spark ignition engines equipped with the Common Rail (CR) system strongly improve engine performance in terms of fuel consumption and pollutant emission reduction. As a drawback the fuel pressure in the rail has to be kept as constant as possible to the demanded pressure working set-points in order to achieve the advantages promised by this technology. In this work a Model Reference Adaptive Control (MRAC) algorithm based on the Minimal Control Synthesis (MCS) strategy is proposed to reduce the residual pressure in the rail. Numerical results based on a CR mean value model, previously proposed in the literature and experimentally validated, show that a very satisfactory attenuation of the pressure ripple as well as pressure tracking are attained in different working conditions. A quantitative comparison with a classical gain scheduling model-based control approach confirms furthermore the effectiveness of the proposed adaptive control strategy.

An approach to fault detection (FD) in industrial measurement systems is proposed in this paper which includes an identification strategy for early detection of the appearance of a fault. This approach is model based, i.e. nominal models are used which represent the fault-free state of the on-line measured process. This approach is also suitable for off-line FD. The framework that combines FD with isolation and correction (FDIC) is outlined in this paper. The proposed approach is characterized by automatic threshold determination, ability to analyse local properties of the models, and aggregation of different fault detection statements. The nominal models are built using data-driven and hybrid approaches, combining first principle models with on-line data-driven techniques. At the same time the models are transparent and interpretable. This novel approach is then verified on a number of real and simulated data sets of car engine test benches (both gasoline-Alfa Romeo JTS, and diesel-Caterpillar). It is demonstrated that the approach can work effectively in real industrial measurement systems with data of large dimensions in both on-line and off-line modes.

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