Control Systems (CS)

Main research interest (DSC/e related)

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

Success stories

  • 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.

Project examples

  • 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
    (DISC/NWO, 2012-2016).
  • INSPEC Integrating sensor-based process monitoring and advanced process control
    (TKI-ISPT, 2017-2021).