Modeling, Design, and Simulation of Systems with Uncertainties (Mathematical Engineering) - Tapa dura

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Andreas Rauh

received his diploma degree in electrical engineering and information technology from the Technische Universität München, Munich, Germany, in 2001 and his PhD degree (Dr.-Ing.) from the University of Ulm, Germany, in 2008. His research interests are: State and parameter estimation for stochastic and set-valued uncertainties, verified simulation of nonlinear uncertain systems, nonlinear, robust, and optimal control, interval methods for ordinary differential equations as well as differential-algebraic systems. Currently, he is with the Chair of Mechatronics, University of Rostock, Germany, as post-doctoral researcher.

Ekaterina Auer

received her Diplomas in Mathematics and Computer Science from Ulyanovsk State University in 2001 and from the University of Duisburg-Essen in 2002. Since 2002, she has been working at the chair for computer graphics and scientific computing at the University of Duisburg-Essen as a research assistant, receiving her Ph.D. in 2006. Her main interests are scientific computing **and development of software for its application to problems in mechanics and engineering.

De la contraportada

To describe the true behavior of most real-world systems with sufficient accuracy, engineers have to overcome difficulties arising from their lack of knowledge about certain parts of a process or from the impossibility of characterizing it with absolute certainty. Depending on the application at hand, uncertainties in modeling and measurements can be represented in different ways. For example, bounded uncertainties can be described by intervals, affine forms or general polynomial enclosures such as Taylor models, whereas stochastic uncertainties can be characterized in the form of a distribution described, for example, by the mean value, the standard deviation and higher-order moments.

The goal of this Special Volume on Modeling, Design, and Simulation of Systems with Uncertainties is to cover modern methods for dealing with the challenges presented by imprecise or unavailable information. All contributions tackle the topic from the point of view of control, state and parameter estimation, optimization and simulation.

Thematically, this volume can be divided into two parts. In the first we present works highlighting the theoretic background and current research on algorithmic approaches in the field of uncertainty handling, together with their reliable software implementation. The second part is concerned with real-life application scenarios from various areas including but not limited to mechatronics, robotics, and biomedical engineering.