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

Informazioni relative al progetto

L2C

ID dell’accordo di sovvenzione: 864017

DOI

10.3030/864017

Data della firma CE 23 Marzo 2020

Data di avvio 1 Settembre 2020

Data di completamento 31 Agosto 2026

Finanziato da

• EXCELLENT SCIENCE - European Research Council (ERC)

Costo totale

€ 1 911 250,00

Contributo UE

€ 1 911 250,00

Coordinato da

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)

Periodo di rendicontazione: 2023-09-01 al 2025-02-28

Sintesi del contesto e degli obiettivi generali del progetto

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.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

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.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

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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