Work

Dionysos.jl: a Modular Platform for Smart Symbolic Control

Year: 2024

Type: article

Abstract: We introduce Dionysos.jl,a modular package for solving optimal control problems for complex dynamical systems using stateof-the-art and experimental techniques from symbolic control, optimization, and learning.More often than not with Cyber-Physical systems, the only sensible way of developing a controller is by discretizing the different variables, thus transforming the control task into a purely combinatorial problem on a finite-state mathematical object, called an abstraction of this system.Although this approach offers a safety-critical framework, the available techniques suffer important scalability issues.In order to render these techniques practical, it is necessary to construct smarter abstractions that differ from classical techniques by partitioning the state-space in a non trivial way.

Source: JuliaCon Proceedings

DOI: 10.21105/jcon.00160

Authors: Julien Calbert, Adrien Banse, Benoît Legat, Raphaël Jungers

Language: English

Topic: Formal Methods in Verification

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: European Commission, Fonds De La Recherche Scientifique - FNRS, Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture, H2020 European Research Council

Awards: G2115076930, Learning to Control - Smart and Data-Driven Formal Methods for Cyber-Physical Systems control, G434337789

OpenAlex ID: W4405378153