A robust non-linear multivariable control design with integral action is proposed and investigated for control of EGR valve and VGT position in heavy duty diesel engines. The main control goal is to regulate oxygen/fuel ratio and intake manifold EGR-fraction. These are chosen as main performance variables since they are strongly coupled to the emissions. A recently developed non-linear control ...

The production of torque and pollutants of Variable Valve Actuation equipped internal combustion engines found in the automotive industry (both Diesel and gasoline engines) is studied. Variable Valve Actuation (VVA) is a technology which has been introduced to optimize engine efficiency at steady-states covering a wide range of operating conditions. In more details, the outcome of the internal ...

The production of torque and pollutants of Variable Valve Actuation equipped internal combustion engines found in the automotive industry (both Diesel and gasoline engines) is studied. Variable Valve Actuation (VVA) is a technology which has been introduced to optimize engine efficiency at steady-states covering a wide range of operating conditions. In more details, the outcome of the internal ...

Homojen dolgulu sıkıştırma ile ateşlemeli (HCCI) motorların yüksek termik verimleri ve düşük NOx emisyonları sebebiyle buji (SI) (CI) motorlara göre önemli üstünlükleri bulunmaktadır. Ancak HCCI motorlarda yanma başlangıcının kontrol edilmesi oldukça zordur. Bu çalışmada, dört silindirli SI motordan dönüştürülmüş olan bir motoru kullanılmıştır. 373 K hava giriş sıcaklığı için, emme manifold bas...

In this paper, we constrain faces to points on a manifold within the parameter space of a linear statistical model. The manifold is the subspace of faces which have maximally likely distinctiveness and different points correspond to unique identities. We show how the tools of differential geometry can be used to replace linear operations such as warping and averaging with operations on the surf...

This paper investigates an approach to model the space of brain images through a low-dimensional manifold. A data driven method to learn a manifold from a collections of brain images is proposed. We hypothesize that the space spanned by a set of brain images can be captured, to some approximation, by a low-dimensional manifold, i.e. a parametrization of the set of images. The approach builds on...