Industrial Systems
The Industrial Systems Group is researching various topics regarding the automation of industrial systems in order to increase the performance as well as the effectiveness of such systems. The focus lies on modelling and control, especially with the objective to apply theoretical control approaches on industrial applications.
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Toughout to the various topics, different challenges arise:
- Most technical systems are complex, non-linear or in certain circumstances exhibit position-dependent/ spatial dynamics.
- Today, many industrial applications are not controlled. Increasing the degree of automation can result in an increased productivity.
- The required computing capacity is usually not available in direct proximity to the process. Edge/Cloud-Computing can solve this issue but poses many new challenges like time delays rooted in the communication setup.
- Processes are influenced by external disturbances or even exhibit time-variant dynamics.
- Often, industrial systems can only be modelled with the aid of multidisciplinary approaches, e.g. with the aid of fluid mechanics, electro mechanics, etc. Due to the complexity of the models, either a model reduction or a simplified description of the system’s dynamic behavior is necessary for control purposes.
- Relevant process values are often not measureable because a suitable sensor is either not available or too expensive.
- Technical limitations or process constraints must be considered explicitly for the control task.
- Higher and sometimes conflicting objectives, e. g. quality, processing time, energy consumption, have to be balanced
In order to address these challenges the Industrial Systems Group researches, in cooperation with interdisciplinary partners from industry and research, on control solutions, which extend well beyond the state of the art. Amongst others, these include:
- Model-based optimization approaches for complex systems.
- The Application of real-time model-based predictive control and continuously learning control.
- Automated system identification methods for complex subsystems, which represent the relevant system dynamics. Application of neural networks to control.
- Control design and analysis of distributed parameter systems.
- Development of reduced white-box and grey-box models.
- Development of virtual sensors (observers) in order to estimate not measurable process values.
- Rapid control prototyping and implementation of control strategies on industrial control systems.
The following issues are currently being researched:
- Cluster of excellence “Internet of Production”
- Control of mechatronic off-shore systems
- Active damping of building structures
- Control of climate equipment and process plants
- Modelling and control of fluid-dynamical systems