Roland Toth

Machine Learning for Modelling and Control

Focus on data-driven modelling (identification) and control of complex physical/chemical systems, in particular in the high-tech and process technology domains.


Research Profile

The research activities aim at efficiently addressing modelling and control of nonlinear/time-varying behavior of systems in these domains by developing a fusion of system identification, control and machine learning methods. The resulting methods automatically construct dynamical models capturing user specified aspects of the system behavior. In terms of control, policies/algorithms are automatically synthesized that realize a desired behavior of a system by manipulating its actuators. A strong emphasis is put on data-driven structural exploration of the underlying system dynamics, like identification of structured nonlinear systems, and data-driven synthesis of control polices. In this exploration, learning the associated model accuracy/control performance versus complexity trade-off plays an important role. Another focus of the research activities is the development of automated methods that use of surrogate models with linear, but varying dynamical representation concepts, such as linear parameter-varying models, to facilitate technological evolution of currently wide-spread methodologies based on the linear time-invariant framework in engineering.

Meet some of our Researchers

Most important active projects

ERC project: Automated Linear Parameter-Varying Modeling and Control Synthesis for Nonlinear Complex Systems []

MSC-IF: NL2LPV - Nonlinear system modelling for linear parameter-varying control design []

TTW project, Embedded systems: Control and data-driven modeling using Symbolic methods (CADUSY)

TTW project, HTSM: Nanometer-accurate planar actuation system (NAPAS)


  • Visiting address

    Groene Loper 19
    5612 AP Eindhoven
  • Postal address

    Department of Electrical Engineering
    P.O. Box 513
    5600 MB Eindhoven