Machine Learning for Modelling and Control

  • Groene Loper 19, Flux

    5612 AP Eindhoven

    Netherlands

  • P.O. Box 513, Department of Electrical Engineering

    5600 MB Eindhoven

    Netherlands

Organization profile

Introduction / mission

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

Organisational 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.

Fingerprint Dive into the research topics where Machine Learning for Modelling and Control is active. These topic labels come from the works of this organisation's members. Together they form a unique fingerprint.

  • Network Recent external collaboration on country level. Dive into details by clicking on the dots.

    Projects

    Research Output

    Automating data-driven modelling of dynamical systems: an evolutionary computation approach

    Khandelwal, D., 4 Mar 2020, Eindhoven: Technische Universiteit Eindhoven. 255 p.

    Research output: ThesisPhd Thesis 1 (Research TU/e / Graduation TU/e)

    Open Access
    File

    Extending the Best Linear Approximation framework to the process noise case

    Schoukens, M., Pintelon, R. M., Dobrowiecki, T. & Schoukens, J., Apr 2020, In : IEEE Transactions on Automatic Control. 65, 4, p. 1514-1524 11 p., 8736761.

    Research output: Contribution to journalArticleAcademicpeer-review

  • Nanometer-accurate motion control of moving-magnet planar motors

    Proimadis, I., 30 Apr 2020, Eindhoven: Technische Universiteit Eindhoven. 278 p.

    Research output: ThesisPhd Thesis 1 (Research TU/e / Graduation TU/e)

    Open Access
    File

    Activities

    • 4 Editorial activity
    • 1 Conference

    ECC (Publisher)

    Maarten Schoukens (Editorial board member)
    2019 → …

    Activity: Publication peer-review and editorial work typesEditorial activityScientific

    IEEE-CSS (Publisher)

    Maarten Schoukens (Editorial board member)
    2019 → …

    Activity: Publication peer-review and editorial work typesEditorial activityScientific

    3rd IFAC Workshop on Linear Parameter-Varying Systems

    Maarten Schoukens (Organiser)
    2019

    Activity: Participating in or organising an event typesConferenceScientific

    Student theses

    Advanced adaptive space discretization for special element method applied to electromechanical devices

    Author: Geerlofs, M., 30 Aug 2018

    Supervisor: Lomonova, E. (Supervisor 1), Wijnands, K. (Supervisor 2), Krop, D. (Supervisor 2), Toth, R. (Supervisor 2) & Curti, M. (Supervisor 2)

    Student thesis: Master

    Linear parameter varying control of nonlinear systems

    Author: Sharif, B., 30 Aug 2018

    Supervisor: Toth, R. (Supervisor 1) & Mazzoccante, G. S. (Supervisor 2)

    Student thesis: Master

    Nonlinear tracking and rejection using linear parameter-varying control

    Author: Koelewijn, P., 2018

    Supervisor: Toth, R. (Supervisor 1)

    Student thesis: Master