Search Results for author:
Found 7 papers, 0 papers with code
Learning stability guarantees for constrained switching linear systems from noisy observations
We present a data-driven framework based on Lyapunov theory to provide stability guarantees for a family of hybrid systems.
Learning stability of partially observed switched linear systems
In this paper, we propose an algorithm for deciding stability of switched linear systems under arbitrary switching based purely on observed output data.
Black-box stability analysis of hybrid systems with sample-based multiple Lyapunov functions
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.
Learning stability guarantees for data-driven constrained switching linear systems
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.
Data-driven stability analysis of switched affine systems
We consider discrete-time switching systems composed of a finite family of affine sub-dynamics.
Computation of invariant sets via immersion for discrete-time nonlinear systems
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.
Data-driven computation of invariant sets of discrete time-invariant black-box systems
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.
