Learning to Control - Smart and Data-Driven Formal Methods for Cyber-Physical Systems control

Información del proyecto

L2C

Identificador del acuerdo de subvención: 864017

DOI

10.3030/864017

Fecha de la firma de la CE 23 Marzo 2020

Fecha de inicio 1 Septiembre 2020

Fecha de finalización 31 Agosto 2026

Financiado con arreglo a

• EXCELLENT SCIENCE - European Research Council (ERC)

Coste total

€ 1 911 250,00

Aportación de la UE

€ 1 911 250,00

Coordinado por

UNIVERSITE CATHOLIQUE DE LOUVAIN
Belgium

Periodic Reporting for period 3 - L2C (Learning to Control - Smart and Data-Driven Formal Methods for Cyber-Physical Systems control)

Período documentado: 2023-09-01 hasta 2025-02-28

Resumen del contexto y de los objetivos generales del proyecto

L2C is a pluridisciplinary project at the frontier between Control Engineering, Computer Science
and Applied Mathematics.Its goal is to develop automatic control techniques for the emerging Cyber-Physical systems (think of self-driving cars, smart grids, or coordinated moving robots), for which classical automation techniques turn out to be inefficient.


L2C impacts both fundamental Science and Engineering, as the theoretical
research is driven and fostered by cutting edge technological challenges.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

We have gathered recent fundamental advances in the theory of control, and showed that it may be advantageously combined into a state-of-the-art, off-the-shelf, automatic controller design methodology, that can tackle complex systems, and even data-driven systems.
Until now, our advances are mainly theoretical, but we also have developed a couple of proofs-of-concept showing that we are in the good direction.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

We have developed, or generalized, symbolic control, and data-driven control techniques, whose goal is to automatically control an arbitrarily complex control system, by representing it with 'symbolic tools', which is a representation that a computer can naturally handle. We have both developed new techniques, and generalized classical techniques to complex systems. We will now put all these techniques under a common umbrella, and implement this toolbox in a common software solution, which will be seamlessly usable by engineers, without having to rely on complex theories from systems and control.

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