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
Projects
Meet some of our Researchers
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
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Lucian Cristian Iacob,Roland Tóth,Maarten Schoukens
Koopman form of nonlinear systems with inputs
Automatica (2024) -
C. Verhoek,Patrick J.W. Koelewijn,Sofie Haesaert,Roland Tóth
Direct data-driven state-feedback control of general nonlinear systems
(2024) -
J.J. Kon,Jeroen J.M. van de Wijdeven,Dennis J.H. Bruijnen,Roland Tóth,Marcel F. Heertjes,Tom A.E. Oomen
Direct Learning for Parameter-Varying Feedforward Control
(2024) -
M.F. (Fahim) Shakib,Roland Tóth,A.Y. (Sasha) Pogromskiy,A. Pavlov,Nathan van de Wouw
Kernel-based learning of stable nonlinear state-space models
(2024) -
Mircea Lazar,Maarten Schoukens
Nonlinear Data-Driven Predictive Control Using Deep Subspace Prediction Networks
(2024)
Contact
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Visiting address
FluxGroene Loper 195612 AP EindhovenNetherlands -
Visiting address
FluxGroene Loper 195612 AP EindhovenNetherlands -
Postal address
P.O. Box 513Department of Electrical Engineering5600 MB EindhovenNetherlands -
Postal address
P.O. Box 513Department of Electrical Engineering5600 MB EindhovenNetherlands