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Although the application of cylinder pressure sensors to gain insight into the combustion process is not a novel topic itself, the recent availability of inexpensive in-cylinder pressure sensors has again prompted an upcoming interest for the utilization of the cylinder pressure signal within engine control and monitoring. Besides the use of the in-cylinder pressure signal for combustion analysis and control the information can also be used to determine related quantities in the exhaust or intake manifold. Within this work two different methods to estimate the pressure inside the exhaust manifold are proposed and compared. In contrary to first principle based approaches, which may require time extensive parameterization, alternative data driven approaches were pursued. In the first method a Principle Component Analysis (PCA) is applied to extract the cylinder pressure information and combined with a polynomial model approach.

The topic of this paper is the identification of a high-precision model for Magneto-Rheological (MR) dampers. A semi-active MR-damper can be seen as a non-linear system, where the inputs are the stroke-velocity and the command current; the current is the control input which modulates at high-bandwidth the damping characteristic through the variation of a magnetic field. The output is the force delivered by the damper. Among the broad set of applications where MR-dampers can be used, this work mainly focuses on MR-dampers for the control of vehicle dynamics (trains, road vehicles, tractors, etc.). High-precision models of MR dampers can be designed using two different model classes: gray-box models (also called semi-physical models) and black-box models. Both approaches are considered in this work.

The problem considered in this paper is the design and analysis of the optimal control strategy for comfort-oriented semi-active suspensions in road vehicles. The goal of this work is to study and analyze the best control algorithm, and to use it as a benchmark. It is shown that the best control algorithm is an on-off switching strategy, which is designed using the principles of optimal and predictive control, under the assumption that the road disturbance can be predicted over a short time-horizon. Even though this optimal control strategy can be computationally intensive in a real-time application, it sets a lower bound on the best possible attenuation performance achievable by semi-active damping control.

In the last few years, extended research has been performed by car manufacturers in order to improve the handling, safety and comfort of ground vehicles. As a results, modern cars are equipped with many electronic automatic control devices. Among them the yaw-control system is probably the most critical and challenging. Road vehicles are complex non-linear dynamic systems, and the control of some of their motions needs the development of adequate full-vehicle models. In this paper a complete dynamic model of a road vehicle is derived and discussed, which can be suitably used to develop the new concept of “performance-oriented” yaw-control system.

Especially in view of more and more stringent emission legislation in passenger cars it is required to reduce the amount of pollutants. In the case of Diesel engines mainly NOx and PM are emitted during engine operation. The main influence factors for these pollutants are the in-cylinder oxygen concentration and the injected fuel amount. Typically the engine control task can be divided into two separate main parts, the fuel and the air system. Commonly air system control, consisting of a turbocharger and exhaust gas recirculation control, is used to provide the required amount of oxygen and address the emission targets, whereas the fuel is used to provide the desired torque. Especially in transient maneuvers the different time scales of both systems can lead to emission peaks which are not desired. Against this background in this work instead of the common way to address the air system, the fuel system is considered to reduce emission peaks during transients.