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When a diesel engine is running at steady state, the diesel combustion process still has some level of variation from cycle to cycle, even if engine load and all control inputs are fixed. This variation is a disturbance for the speed governor, and it could lead to less than optimal engine performance in terms of fuel economy, exhaust gas emission and noise emission. The most effective way to reduce this steady state combustion variation is by applying fuel path feedback control. The control action can be performed at a fixed frequency, or at a defined cycle event time. Intra-cycle control has the highest capacity to suppress the combustion deviation, as it measures the current cycle combustion performance and compensates for it within the same cycle using a very fast control response. Correct knowledge and a model of the disturbance sources and combustion variation patterns are essential in the design process of this intra-cycle control strategy.

Measurement accuracy and repeatability for fuel rate is the key to successfully improve fuel economy of diesel engines as fuel economy could only be achieve by precisely controlling air/fuel ratio and monitor real-time fuel consumption. The volumetric and gravimetric measurement principles are well-known methods to measure the fuel consumption of internal combustion engines. However, the fuel flow rate measured by these methods is not suitable for either real-time control or real-time measurement purposes. The problem concerning discontinuous data of fuel flow rate measured by using an AVL 733s fuel meter was solved for the steady state scenario by using neural networks. It is easier to choose inputs of the neural networks for the steady state scenario because the inputs could be chosen as the particular inputs which excited the system in the application.

More and more stringent emission regulations require advanced control technologies for combustion engines. This goes along with increased monitoring requirements of engine behaviour. In case of emissions behaviour and fuel consumption the actual combustion efficiency is of highest interest. A key parameter of combustion conditions is the in-cylinder pressure during engine cycle. The measurement and detection is difficult and cost intensive. Hence, modelling of in-cylinder conditions is a promising approach for finding optimum control behaviour. However, on-line controller design requires real-time scenarios which are difficult to model and current modelling approaches are either time consuming or inaccurate. This paper presents a new approach of in-cylinder condition prediction. Rather than reconstructing in-cylinder pressure signals from vibration transferred signals through cylinder heads or rods this approach predicts the conditions.