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

Projektinformationen

L2C

ID Finanzhilfevereinbarung: 864017

DOI

10.3030/864017

EK-Unterschriftsdatum 23 März 2020

Startdatum 1 September 2020

Enddatum 31 August 2026

Finanziert unter

• EXCELLENT SCIENCE - European Research Council (ERC)

Gesamtkosten

€ 1 911 250,00

EU-Beitrag

€ 1 911 250,00

Koordiniert durch

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)

Berichtszeitraum: 2023-09-01 bis 2025-02-28

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

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.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

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.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

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.

image-l2c-1.jpg