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Cylinder pressure signals contain valuable information for closed loop engine control. For using this information low-cost cylinder pressure sensors with high long-term stability have been developed and are starting to be installed into production engines [1,2,3,4]. This paper presents new algorithms to estimate cylinder air mass, to control exhaust gas recirculation and to estimate the air-fuel ratio distribution between different cylinders of a SI engine. The proposed control algorithms avoid additional calibration expense by using adaptive, model based control strategies and learning feed-forward control. They have been implemented on a dSPACE rapid control prototyping system [5] and have been evaluated through studies using a 1.0 liter 3 cylinder SI engine on a dynamic engine test stand.

The complexity of the air path of modern common rail diesel engines is rapidly increasing and simultaneously, the demand on air and turbocharger control performances is becoming more challenging. To meet the upcoming emission regulations, the usage of a low pressure exhaust gas recirculation (EGR) circuit in addition to the standard high pressure EGR circuit is often considered. This kind of architecture usually requires a more sophisticated air control system in which a precise control of the EGR flow delivered by the two recirculation branches is required. Moreover, as an alternative or in addition to the low pressure EGR, the implementation of a NOx reduction system e.g. a NOx trap is possible. To proper maintain the correct efficiency of this kind of after-treatment system, special regeneration strategies are adopted where a rich combustion is used instead of the standard Diesel lean mode.

Because of increasing requirements for low emissions and fuel consumption, combustion engines are getting more and more control inputs, like multiple injection, exhaust gas recirculation (EGR), turbocharger valve position (TVP), variable valve timing (VVT), etc. With the addition of manipulated variables, the required measurement time for obtaining the steady-state characteristics and control look-up tables rises exponentially. A comprehensive design of the measurement experiment is becoming more and more essential. The objective is to measure the engine characteristics and properties with a minimum number of measurement points (with firstly concentrating on the stationary behavior). A new methodology is presented to automatically determine characteristic mappings by incorporating prior knowledge. Since physical modeling of the engine behavior is mostly not appropriate, prior knowledge for experimental design is derived by evaluating measurement data.