Control Systems (CS)
As part of the systems and control field, that is targeted at controlling and optimizing the operational performance of dynamical systems, the data-related research of CS focusses on data-driven modelling of dynamical systems involving:
- Linear and nonlinear (continuous) dynamics
- Experiment design
- Model uncertainty quantification
- Approximate modelling and model reduction
- Structured systems, in both interconnections and physics-based dynamics
- Performance monitoring and predictive maintenance
- On-line model (parameter) estimation
- Adaptation and learning; data analytics
Driven by applications in:
- High-tech mechatronic systems
- Industrial process control systems
- Power networks and energy systems
- Automotive / smart mobility systems
- Framework for data-driven modelling in dynamic networks.
- Basic reference for identification of Linear parameter-varying models (Springer).
- EU-FP7 project Autoprofit -Advanced autonomous model-based operation of industrial process systems (2013-2014).
- Toolset for tensor decompositions.
- 2 ERC projects granted in 2016.
- SYSDYNET Data-driven modelling in dynamic networks (Van den Hof)
ERC-Advanced Research project (2016-2021).
- APROCS Automated linear parameter-varying modeling and control synthesis for nonlinear complex systems
ERC Starting Research project (2017-2022).
Verification and control of physical systems, data-driven and model-based approaches
- INSPEC Integrating sensor-based process monitoring and advanced process control