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

Project Information

L2C

Grant agreement ID: 864017

DOI

10.3030/864017

EC signature date 23 March 2020

Start date 1 September 2020

End date 31 August 2026

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 911 250,00

EU contribution

€ 1 911 250,00

Coordinated by

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)

Reporting period: 2023-09-01 to 2025-02-28

Summary of the context and overall objectives of the project

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.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

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.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

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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