no code implementations • 22 Apr 2024 • Julien Calbert, Adrien Banse, Benoît Legat, Raphaël M. Jungers
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 control, optimization, and learning.
no code implementations • 30 Mar 2023 • Adrien Banse, Licio Romao, Alessandro Abate, Raphaël M. Jungers
In order to learn the optimal structure, we define a Kantorovich-inspired metric between Markov chains, and we use it as a loss function.
no code implementations • 10 Feb 2023 • 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.
no code implementations • 19 Jan 2023 • Zheming Wang, Raphaël M. Jungers, Mihály Petreczky, Bo Chen, Li Yu
In this paper, we propose an algorithm for deciding stability of switched linear systems under arbitrary switching based purely on observed output data.
no code implementations • 4 Dec 2022 • Adrien Banse, Licio Romao, Alessandro Abate, Raphaël M. Jungers
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.
no code implementations • 2 May 2022 • Adrien Banse, Zheming Wang, Raphaël M. Jungers
More precisely, our contribution is the following: we derive a probabilistic upper bound on the CJSR of an unknown CSLS from a finite number of observations.
no code implementations • 2 May 2022 • Adrien Banse, Zheming Wang, Raphaël M. Jungers
By generalizing previous results on arbitrary switching linear systems, we show that, by sampling a finite number of observations, we are able to construct an approximate Lyapunov function for the underlying system.
1 code implementation • 13 Apr 2022 • Antoine Aspeel, Amaury Gouverneur, Raphaël M. Jungers, Benoit Macq
We prove that in terms of expected mean square error, the stochastic program filter outperforms the online filter, which itself outperforms the offline filter.
no code implementations • 14 Mar 2022 • Matteo Della Rossa, Lucas N. Egidio, Raphaël M. Jungers
These results reveal the main differences and specificities of switched affine systems with respect to linear ones, providing a first step for the analysis of switched systems composed by subsystems not sharing the same equilibrium.
no code implementations • 23 Sep 2021 • Matteo Della Rossa, Zheming Wang, Lucas N. Egidio, Raphaël M. Jungers
We consider discrete-time switching systems composed of a finite family of affine sub-dynamics.
no code implementations • 2 Aug 2021 • Guillaume O. Berger, Raphaël M. Jungers
We study the algorithmic complexity of the problem of deciding whether a Linear Time Invariant dynamical system with rational coefficients has bounded trajectories.
no code implementations • 18 Jan 2021 • Benoît Legat, Raphaël M. Jungers
We reformulate the invariance of a set as an inequality for its support function that is valid for any convex set.
Optimization and Control 93D15, 93D30, 93B40, 93B05, 93B25, 93B40 F.2.1; G.1.6
no code implementations • 2 Oct 2020 • Zheming Wang, Raphaël M. Jungers, Chong-Jin Ong
In this paper, we propose an approach for computing invariant sets of discrete-time nonlinear systems by lifting the nonlinear dynamics into a higher dimensional linear model.
no code implementations • 28 Jul 2019 • Zheming Wang, Raphaël M. Jungers
A data-driven framework relying on the observation of trajectories is proposed to compute almost-invariant sets, which are invariant almost everywhere except a small subset.