Hardware in the Loop

Real-time modeling of complex systems and rapid prototyping at the departmental HIL simulator

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Process Analysis via Machine Learning

Detection of dependencies of quality and performance variables on process parameters

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Analysis of Medical Data

Analysis software as an integral part of a multifunctional clinical monitoring system

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Control Concepts for Future Power Networks

Multi-network model-based monitoring and control for the planning and operation of a power distribution network

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Predictive Maintenance

Optimize maintenance planning with Machine Learning

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System Analysis, Prognosis and Control

Our focus is on real-time plant operation and drive technology in production and power generation.

With physical knowledge, but also based on pure measurement data, we create digital twins for multiphysical, dynamic systems. In the data-driven acquisition of information, we can compensate for the interference superimpositions of measurement data that occur in reality by means of suitable mathematical methods in order to achieve better analyses and forecasts of system behaviour.

The digital twin forms the basis for many applications:

We support our customers in the conceptual design, data analysis and operation of the analysis, forecasting or control systems. We implement new concepts, e.g. the use of low latencies using 5G communication for data transmission between sensors, controllers and actuators.

To address these issues, we use methods from systems and control theory as well as machine learning, especially deep learning.

Fields of Activity


Power Generation and Distribution

Modeling, monitoring and control of energy producers, energy distribution networks and energy efficiency of consumers



Machine Monitoring and Control

Electronic, mechanical and mechatronic systems, facilities and machines


Bio-sensors and Medical Devices

Modeling and data analysis in the fields of system biology and medical engineering

A Short Interview with...

the Head of Department Andreas Wirsen.

Information Material

Flyers and other information material of the department.



Machine Learning

Problem-solving for various industrial aims by using methods of machine learning


Controller Design for Complex Systems

Control algorithms from standard PID-control to complex distributed and model-predictive controllers


Model Identification and State Estimation

Wide method portfolio of Kalman Filtering, Monte Carlo methods, nonlinear optimization and many more