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 inter-pretability and generalization capabilities form important roadblocks for a wide adaptation and ap-plicability of nonlinear system identification methods.
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 black-box system identification. COMPLETE 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 black-box 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.
These objectives are far beyond the actual abilities of system identification, lifting the model completion for dynamical systems from ad-hoc approaches to a systematic, flexible, theoretically supported framework. My leading expertise on structured nonlinear system identification, and recent proof-of-concept results ensure the feasibility of the project. The resulting system identification framework is applicable over a wide range of engineering disciplines (mechanical, electrical, biomedical) and pro-vides system engineers with the necessary insight to guide them towards better solutions for tomor-row's industry.
