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.

News

March 3, 2023
Data-driven linear parameter-varying control
Tom Bloemers defended his PhD thesis at the department of Electrical Engineering on March 3rd.
Read more
February 16, 2023
Advancing control engineering methods to meet current and future challenges
Patrick Koelewijn defended his PhD thesis cum laude at the department of Electrical Engineering on February 15th.
Read more
More news
Embarking on a new adventure with a huge European grant
November 22, 2022
Nanometer accuracy: development of high-precision motion systems
April 29, 2020
Helping non-experts create mathematical models through natural selection
March 3, 2020

Projects

Research project

Data-Augmented Modeling Of Constitutive Laws for Engineering Systems

Meet some of our Researchers

Doctoral Candidate

Sarvin Moradi

Associate Professor

Roland Toth

Doctoral Candidate

Cristi Iacob

Postdoc

Tom Bloemers

Doctoral Candidate

Yorick Broens

Assistant Professor

Maarten Schoukens

University Researcher

Patrick Koelewijn

Doctoral Candidate

Chris Verhoek

Doctoral Candidate

Gerben Beintema

Doctoral Candidate

Clarisse Bosman Barros

Most important active projects

ERC project: Automated Linear Parameter-Varying Modeling and Control Synthesis for Nonlinear Complex Systems [https://research.tue.nl/en/prizes/automated-linear-parameter-varying-modeling-and-control-synthesis]

MSC-IF: NL2LPV - Nonlinear system modelling for linear parameter-varying control design [https://research.tue.nl/en/prizes/marie-sk%C5%82odowska-curie-individual-fellowship]

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

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

Recent Publications

Our most recent peer reviewed publications

Contact

  • Visiting address

    Flux
    Groene Loper 19
    5612 AP Eindhoven
    Netherlands

  • Postal address

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