Real-time Plant Operation and Drive Technology

The increasing integration level of nanoscale semiconductor circuits as well as the connection of various sensors and actuators significantly increase the complexity and sensitivity of the system behavior of many electronic and mechatronic applications.

Many task settings therefore require a mathematical modeling of the respective system, e.g. by the method of finite elements. Due to the often very high model complexity, model-order reduction techniques are used to generate the required real-time capability. Then, the resulting models can be the basis of model-based controllers or state estimators, as well as be used to simulate the corresponding components by means of simulation in the software-in-the-loop or hardware-in-the-loop setting.

Active vibration damping or noise reduction in automotive applications, signal tracking for test bench or temperature controls and controller design for smart energy applications are typical tasks in the area of ​​control.

The central question in the field of condition monitoring is the vibration prognosis and analysis of rotating drives. In particular, the working group has many years of experience in the field of torsional monitoring of rotating machines, especially in the case of power plant generator shafts.

We would like to advise and support you in

  • Modeling and simulation of the complete multi-physical system
  • Design of model-based and robust observers/controllers
  • Analysis of the performance and robustness of the complete dynamical system
  • Integration of observers and controllers into your system hardware
  • Rapid Prototyping
  • Development of innovative complete system solutions e.g. consisting of the hardware, controllers/observers and intuitive user interfaces

 

Example Projects

 

Process Control and Monitoring Using Edge Computing

The »EMILIE« project focuses on improving decentralized data acquisition and processing through edge gateways.

 

Hybrid Backward Computing for the Plastics Industry

In the HyTwin project, we are developing a hybrid approach for a data-based digital twin.

 

Predictive Maintenance

Optimize maintenance planning with Machine Learning

 

Intelligent Control in Beverage Production

BMWi-Project DESPRIMA – Demand Side and Production Management for Beverage Filling Processes

 

Hardware in the Loop

Real-time and robust system models for controller design and validation

 

Energy Efficient Annealing Lehr

Model-based control to minimize the energy input of an annealing lehr by using new, innovative actuators

 

Active Damping

Automated controller design for active vibration damping with non-linear actuator behavior

 

Data-based Optimization of Plastics Processing

Energy Efficiency and Flexibility through Digital Twins