Data-driven control of large-scale networks with formal guarantees: A small-gain free approach
This paper offers a data-driven divide-and-conquer strategy to analyze large-scale
interconnected networks, characterized by both unknown mathematical models and …
interconnected networks, characterized by both unknown mathematical models and …
Abstraction-based Control of Unknown Continuous-Space Models with Just Two Trajectories
Finite abstractions (aka symbolic models) offer an effective scheme for approximating the
complex continuous-space systems with simpler models in the discrete-space domain. A …
complex continuous-space systems with simpler models in the discrete-space domain. A …
Learning k-Inductive Control Barrier Certificates for Unknown Nonlinear Dynamics Beyond Polynomials
This work is concerned with synthesizing safety controllers for discrete-time nonlinear
systems beyond polynomials with unknown mathematical models using the notion of k …
systems beyond polynomials with unknown mathematical models using the notion of k …
Data-driven memory-dependent abstractions of dynamical systems via a Cantor-Kantorovich metric
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 …
controllers using simpler, usually discrete, models. In this paper, we propose a data-driven …
Enhancing Data-Driven Stochastic Control via Bundled Interval MDP
The abstraction of dynamical systems is a powerful tool that enables the design of feedback
controllers using a correct-by-design framework. We investigate a novel scheme to obtain …
controllers using a correct-by-design framework. We investigate a novel scheme to obtain …