Systems and control engineers aim to master increasingly complex dynamical systems while including stronger performance, operational and energy constraints. As model-based control design remains the dominant paradigm, this results in an increasing need for nonlinear modeling. However, model interpretability and generalization capabilities form important roadblocks for a wide adaptation and applicability of nonlinear system identification methods. This ERC-funded project aims to tackle these challenges.
Strong prior knowledge is given by existing models, provided by system designers and engineers, even though they do not capture all the nonlinear dynamics of the real-life system. These models are currently not accounted for during data-driven modelling. This project aims to develop a comprehensive nonlinear system identification framework to obtain accurate and interpretable models of measured complex system dynamics by completing an approximate pre-existing model through black-box nonlinear system identification. New theory and algorithms are put in place to 1) provide model structures, algorithms and theory that flexibly interconnect the pre-existing model and the data-driven completion 2) ensure that data-driven completion models are interpretable and preserve key system theoretic aspects 3) data-driven experiment design strategies to detect, quantify and localize model errors at low experimental cost. The resulting system identification methodologies are applicable over a wide range of engineering disciplines (mechanical, electrical, biomedical) and provides system engineers with the necessary insight to guide them towards better solutions for tomorrow's industry.
We are looking for a candidate who meets the following requirements:
The Control Systems (CS) group research activities span all facets of systems and control theory, such as linear, nonlinear and hybrid systems theory, model predictive control, machine learning for modelling and control, modelling and identification and formal methods in control. The CS group has a strong interconnection with other academic institutions and industry via national and European funded projects in a diverse range of application areas, often focusing on interdisciplinary research.
The PhD will join the group and interact with the other members of the CS group (around 40 researchers). Research within the CS Group is characterized by personal supervision. Furthermore, the PhD will have access to the advanced courses offered by the Dutch Institute for Systems and Control, and will be able to attend national and international scientific conferences.
For more detailed information on the activities of the group please check http://tue.nl/cs/.
Do you recognize yourself in this profile and would you like to know more?
Please contact dr. ir. Maarten Schoukens m.schoukens[at]tue.nl.
Have a look in our lab: https://www.youtube.com/watch?v=JaEXPI8gJbU
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