Based on its core competences in system theory and data analytical methods the department deals with the modelling, analysis, prognosis and control of complex system and process behaviour both in the technical and the biological/medical environment.

System Analysis

Starting from process models constructed out of first principles, measurement data and/or expert knowledge mixed symbolical/numerical analysis techniques allow the derivation of fundamental system properties. Model reduction techniques play a central role both for a deeper system understanding, complex system simulations and controller designs.

Prognosis

Prognosis models that were identified considering possible disturbance sources and system uncertainties allow the reliable prediction of the system behaviour in case of unknown inputs. The combination of those prognosis models with interactive visual navigation tools leads to highly efficient multicriterial decision support tools.

Control

Concerning the design of monitoring systems and control algorithms the main focus lies on model based techniques, standard topics like PID-control however are covered as well. Special competences exist in the topic of robust observer and controller design to consider model uncertainties and nonlinear system behaviour e.g. dead time and hysteresis.

Main Subjects

• Monitoring and Control

  

  The research work of this group is focused on the development and implementation of mathematical methods for system modeling and for observer and controller design.

  Main applications are technical systems, such as monitoring of turbine generator shaft lines, active vibration damping within adaptronic systems or temperature control of fluid systems

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• Multiscale structure mechanics

  

  We are mainly working on three subjects here. The first one refers to the numerical computation of the microscopic stress-strain behavior and effective material properties of composite or porous materials.  The second subject deals with contact problems with micro-rough surfaces. Finally, within the third problem complex we consider time-dependent processes for composite bodies, whose macro strength and durability are examined with respect to fatigue, creep strain, impact load, and wear.

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• Dynamic heterogeneous networks

  

  The group dynamic heterogeneous networks is dealing with the modeling and analysis of complex networked systems. This subject (formerly CAD for Analog Circuit Design) has its origin in the field of modeling and analysis of analog circuits. In this context the EDA tool Analog Insydes has been developed.

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• Prognosis of material and product properties

  

  In many complex systems and processes, it is often completely unclear at the beginning on which potential influence factors a selected performance parameter depends. This is due to a lack of adequate physical models. If, however, sufficient representative data is available, e.g., stemming from systematic series of experiments characterizing the input-output behaviour, a system description in the form of a black box or grey box model can be derived by appropriate methods from system identification, data mining, and statistics.

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• Decision support in the medical sciences and in technology

  

  The process of decision making based on complex data can be supported using mathematics and meaningful visualization. Accordingly the activities in this work area lie in the fields Data Mining, knowledge representation and knowledge management. Recent applications belong to computer-assisted medical diagnostics and product engineering in the technical sector.

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