Project Description

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.

Tasks

• Study the literature of machine learning, nonlinear system identification and data-driven modelling.
• Development of (control-oriented) interpretable data-driven nonlinear modeling approaches for model completion.
• Stochastic analysis of consistency and convergence of the results and empirical validation of the techniques on complex physical/chemical and/or electrical/mechatronic systems.
• Exploration of the steps of the identification cycle for the developed methods from experiment design to verification of model completion.
• Dissemination of the results of your research in international and peer-reviewed journals and conferences.
• You will be involved in the research of the PhD students active within the project.
• Assume educational tasks like the supervision of Master students and internships.