Project description
Black-box nonlinear system identification improves model-based design
Models are essential to the design, development and testing of complex dynamical systems, minimising the time and cost of experimentation and prototyping and enhancing system performance. Model-based design has advanced significantly in recent years, yet the models still do not capture the detailed nonlinear dynamics of real-life systems. The ERC-funded COMPLETE project will address this fundamental gap by developing new theory and algorithms that extend the capabilities of a pre-existing approximate model. Specifically, the project will flexibly interconnect the pre-existing model and the black-box completion, ensure the data-driven completion models preserve key system theoretical aspects and develop data-driven experiment design strategies for error handling without the need for extensive experimentation.
Objective
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.
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 provides system engineers with the necessary insight to guide them towards better solutions for tomorrow's industry.
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Keywords
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2022-STG
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5612 AE Eindhoven
Netherlands
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