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Found 13 papers, 1 papers with code
Data-driven abstractions via adaptive refinements and a Kantorovich metric [extended version]
In order to learn the optimal structure, we define a Kantorovich-inspired metric between Markov chains, and we use it as a loss function.
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.
Data-driven memory-dependent abstractions of dynamical systems
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.
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.
Optimal Intermittent Particle Filter
We prove that in terms of expected mean square error, the stochastic program filter outperforms the online filter, which itself outperforms the offline filter.
Stability of Switched Affine Systems: Arbitrary and Dwell-Time Switching
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.
Data-driven stability analysis of switched affine systems
We consider discrete-time switching systems composed of a finite family of affine sub-dynamics.
Complexity of the LTI system trajectory boundedness problem
We study the algorithmic complexity of the problem of deciding whether a Linear Time Invariant dynamical system with rational coefficients has bounded trajectories.
Geometric control of algebraic systems
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
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.
