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

  1. SYSDYNET – Data-driven modelling in dynamic networks (Van den Hof)
    ERC-Advanced Research project (2016-2021)
  2. APROCS – Automated linear parameter-varying modeling and control synthesis for nonlinear complex systems
    ERC Starting Research project (2017-2022)
  3. Verification and control of physical systems,  data-driven and model-based approaches
    (DISC/NWO, 2012-2016)
  4. INSPEC - Integrating sensor-based process monitoring and advanced process control
    (TKI-ISPT, 2017-2021)