4 citations · 11 across the 14 of their papers we have counts for
9 papers · 1 filter
Memory-Dependent Interval Markov Chain Abstractions of Stochastic Dynamics
Menno van Zutphen, Adrien Banse, Domagoj Herceg +2
Finite-state interval Markov-chain (IMC) abstractions provide sound verification and performance bounds for continuous-state stochastic systems by enclosing cell-to-cell transition…
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…
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…
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…
Dionysos.jl: a Modular Platform for Smart Symbolic Control
Julien Calbert, Adrien Banse, Benoît Legat +1
We introduce Dionysos.jl, a modular package for solving optimal control problems for complex dynamical systems using state-of-the-art and experimental techniques from symbolic cont…
Learning stability guarantees for constrained switching linear systems from noisy observations
Adrien Banse, Zheming Wang, Raphaël M. Jungers
We present a data-driven framework based on Lyapunov theory to provide stability guarantees for a family of hybrid systems. In particular, we are interested in the asymptotic stabi…
Data-driven memory-dependent abstractions of dynamical systems
Adrien Banse, Licio Romao, Alessandro Abate +1
We propose a sample-based, sequential method to abstract a (potentially black-box) dynamical system with a sequence of memory-dependent Markov chains of increasing size. We show th…
Black-box stability analysis of hybrid systems with sample-based multiple Lyapunov functions
Adrien Banse, Zheming Wang, Raphaël M. Jungers
We present a framework based on multiple Lyapunov functions to find probabilistic data-driven guarantees on the stability of unknown constrained switching linear systems (CSLS), wh…
Learning stability guarantees for data-driven constrained switching linear systems
Adrien Banse, Zheming Wang, Raphaël M. Jungers
We consider stability analysis of constrained switching linear systems in which the dynamics is unknown and whose switching signal is constrained by an automaton. We propose a data…