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20222026
most citedDionysos.jl: a Modular Platform for Smart Symbolic Control

2 citations · 9 across the 21 of their papers we have counts for

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12 papers · 1 filter

eess.SY2026

End-to-End Abstraction-Based Control with LLM-Enhanced NL-to-LTL Translation

Amir Bayat, Necmiye Ozay, Alessandro Abate +1

Abstraction-Based Controller Design (ABCD) offers a principled framework for the safe control of complex Cyber-Physical Systems (CPSs), but interfacing real-world requirements with…

eess.SY2025

LLM-Enhanced Symbolic Control for Safety-Critical Applications

Amir Bayat, Alessandro Abate, Necmiye Ozay +1

Motivated by Smart Manufacturing and Industry 4.0, we introduce a framework for synthesizing Abstraction-Based Controller Design (ABCD) for reach-avoid problems from Natural Langua…

eess.SY2025

Agile Temporal Discretization for Symbolic Optimal Control

Adrien Janssens, Adrien Banse, Julien Calbert +1

As control systems grow in complexity, abstraction-based methods have become essential for designing controllers with formal guarantees. However, a key limitation of these methods…

eess.SY2025

Memory-dependent abstractions of stochastic systems through the lens of transfer operators

Adrien Banse, Giannis Delimpaltadakis, Luca Laurenti +2

With the increasing ubiquity of safety-critical autonomous systems operating in uncertain environments, there is a need for mathematical methods for formal verification of stochast…

eess.SY2024

Characterizing simulation relations through control architectures in abstraction-based control

Julien Calbert, Antoine Girard, Raphaël M. Jungers

Abstraction-based control design is a promising approach for ensuring safety-critical control of complex cyber-physical systems. A key aspect of this methodology is the relation be…

eess.SY2024

Data-driven memory-dependent abstractions of dynamical systems via a Cantor-Kantorovich metric

Adrien Banse, Licio Romao, Alessandro Abate +1

Abstractions of dynamical systems enable their verification and the design of feedback controllers using simpler, usually discrete, models. In this paper, we propose a data-driven…