L2C | Learning to Control - Smart and Data-Driven Formal Methods for Cyber-Physical Systems control

Summary
The engineered systems surrounding us are increasingly hard to control. Not only the complicated interaction of the physical processes with the machines that control them, but also specifications (cyber-security, safety, privacy, resilience, resource-efficiency, decentralization) are more and more complex, and critical. Last but not least, in an increasing number of situations, no model of the system is available (or the model is too complex), and one needs to ‘learn’ the optimal way of controlling the system by the mere observation of data. Our technological world is living a paradigm shift, which is often coined as the Cyber-Physical Revolution, or the Industry 4.0.

In view of these specificities, the only sensible way of controlling these complex systems is often by discretizing the different variables, thus transforming the model into a simple combinatorial problem on a finite-state automaton, called an abstraction of this system. Until now, this approach has not been proved useful beyond academic, small examples, as it scales very poorly.

The goal of L2C is to transform this approach into an effective, scalable, cutting-edge technology that will address the CPS challenges and unlock their potential. This ambitious goal will be achieved by leveraging powerful tools from Mathematical Engineering. Out of this research, a state-of-the-art software platform will promote our results and translate them into directly usable solutions for the scientific and industrial communities.
L2C is a pluridisciplinary project at the frontier between Control Engineering, Computer Science and Applied Mathematics. It bridges the gap between rich innovative techniques and emerging challenges in Control. It impacts both fundamental Science and Engineering, as the theoretical research is driven and fostered by cutting edge technological challenges.
Unfold all
/
Fold all
More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/864017
Start date: 01-09-2020
End date: 31-08-2026
Total budget - Public funding: 1 911 250,00 Euro - 1 911 250,00 Euro
Cordis data

Original description

The engineered systems surrounding us are increasingly hard to control. Not only the complicated interaction of the physical processes with the machines that control them, but also specifications (cyber-security, safety, privacy, resilience, resource-efficiency, decentralization) are more and more complex, and critical. Last but not least, in an increasing number of situations, no model of the system is available (or the model is too complex), and one needs to ‘learn’ the optimal way of controlling the system by the mere observation of data. Our technological world is living a paradigm shift, which is often coined as the Cyber-Physical Revolution, or the Industry 4.0.

In view of these specificities, the only sensible way of controlling these complex systems is often by discretizing the different variables, thus transforming the model into a simple combinatorial problem on a finite-state automaton, called an abstraction of this system. Until now, this approach has not been proved useful beyond academic, small examples, as it scales very poorly.

The goal of L2C is to transform this approach into an effective, scalable, cutting-edge technology that will address the CPS challenges and unlock their potential. This ambitious goal will be achieved by leveraging powerful tools from Mathematical Engineering. Out of this research, a state-of-the-art software platform will promote our results and translate them into directly usable solutions for the scientific and industrial communities.
L2C is a pluridisciplinary project at the frontier between Control Engineering, Computer Science and Applied Mathematics. It bridges the gap between rich innovative techniques and emerging challenges in Control. It impacts both fundamental Science and Engineering, as the theoretical research is driven and fostered by cutting edge technological challenges.

Status

SIGNED

Call topic

ERC-2019-COG

Update Date

27-04-2024
Images
No images available.
Geographical location(s)
Structured mapping
Unfold all
/
Fold all
Horizon 2020
H2020-EU.1. EXCELLENT SCIENCE
H2020-EU.1.1. EXCELLENT SCIENCE - European Research Council (ERC)
ERC-2019
ERC-2019-COG