157 citations · 551 across the 47 of their papers we have counts for
6 papers · 1 filter
Data-driven stabilization of nonlinear polynomial systems with noisy data
Meichen Guo, Claudio De Persis, Pietro Tesi
In a recent paper we have shown how to learn controllers for unknown linear systems using finite-sized noisy data by solving linear matrix inequalities. In this note we extend this…
Controller design for robust invariance from noisy data
Andrea Bisoffi, Claudio De Persis, Pietro Tesi
For an unknown linear system, starting from noisy open-loop input-state data collected during a finite-length experiment, we directly design a linear feedback controller that guara…
Low-complexity Learning of Linear Quadratic Regulators from Noisy Data
Claudio De Persis, Pietro Tesi
This paper considers the Linear Quadratic Regulator problem for linear systems with unknown dynamics, a central problem in data-driven control and reinforcement learning. We propos…
Data-based stabilization of unknown bilinear systems with guaranteed basin of attraction
Andrea Bisoffi, Claudio De Persis, Pietro Tesi
Motivated by the goal of having a building block in the direct design of data-driven controllers for nonlinear systems, we show how, for an unknown discrete-time bilinear system, t…
Learning control for polynomial systems using sum of squares relaxations
Meichen Guo, Claudio De Persis, Pietro Tesi
This paper considers the problem of learning control laws for nonlinear polynomial systems directly from the data, which are input-output measurements collected in an experiment ov…
Willems' Fundamental Lemma for State-space Systems and its Extension to Multiple Datasets
Henk J. van Waarde, Claudio De Persis, M. Kanat Camlibel +1
Willems et al.'s fundamental lemma asserts that all trajectories of a linear system can be obtained from a single given one, assuming that a persistency of excitation condition hol…