Learning to Control
Smart and DataDriven Formal Methods for CyberPhysical Systems control
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 (cybersecurity, safety, privacy, resilience, resourceefficiency, 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 CyberPhysical 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 finitestate automaton, called an abstraction of this system. Until now, this approach has not been proved useful beyond small academic examples as it scales very poorly.
The goal of L2C is to transform this approach into an effective, scalable, cuttingedge 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 stateoftheart software platform will promote our results and translate them into directly usable solutions for the scientific and industrial communities.
L2Cis 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.
Code
Dionysos is the software of the ERC project Learning to control (L2C). In view of the CyberPhysical Revolution, 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 finitestate automaton, called an abstraction of this system. The goal of L2C is to transform this approach into an effective, scalable, cuttingedge technology that will address the CPS challenges and unlock their potential. This ambitious goal will be achieved by leveraging powerful tools from Mathematical Engineering.
Materials
Repeatability packages

Characterization of templatedependent ordering of graphs in the pathcomplete Lyapunov function framework : computation of (the min, max and Tsum) lifts and simulation relation.
Our team
Publications
 Comparison of PathComplete Lyapunov Functions via TemplateDependent Lifts. arXiv preprint arXiv:2110.13474. .
 Chanceconstrained quasiconvex optimization with application to datadriven switched systems control. In Learning for Dynamics and Control (pp. 571583). PMLR. .
 Abstractionbased branch and bound approach to Qlearning for hybrid optimal control. In Learning for Dynamics and Control (pp. 263274). PMLR. .
 EventTriggered Tracking Control of Networked and Quantized Control Systems. In 2021 European Control Conference (ECC) (pp. 632637). IEEE. .
 Zonotopebased Controller Synthesis for LTL Specifications. arXiv preprint arXiv:2108.00704. .
 Optimal Control for Linear Networked Control Systems with Information Transmission Constraints. arXiv preprint arXiv:2109.10666. .
 Datadriven feedback stabilization of switched linear systems with probabilistic stability guarantees. arXiv preprint arXiv:2103.10823. .
 Datadriven stability analysis of switched affine systems. arXiv preprint arXiv:2109.11169. .
 Alternating simulation on hierarchical abstractions. In 2021 60th IEEE Conference on Decision and Control (CDC) (pp. 593598). IEEE. .
 TemplateDependent Lifts for PathComplete Stability Criteria and Application to Positive Switching Systems. IFACPapersOnLine, 54(5), 151156. .
 Optimal Resource Scheduling and Allocation in Distributed Computing Systems. arXiv preprint arXiv:2112.00708. .
 Optimal measurement budget allocation for particle filtering. In 2020 IEEE International Conference on Image Processing (ICIP) (pp. 15). IEEE. .