13 citations · 29 across the 6 of their papers we have counts for
8 papers · 1 filter
Controller implementability: a data-driven approach
Alberto Padoan, Jeremy Coulson, Florian Dörfler
We study the controller implementability problem, which seeks to determine if a controller can make the closed-loop behavior of a given plant match that of a desired reference beha…
A quantitative and constructive proof of Willems' Fundamental Lemma and its implications
Julian Berberich, Andrea Iannelli, Alberto Padoan +3
Willems' Fundamental Lemma provides a powerful data-driven parametrization of all trajectories of a controllable linear time-invariant system based on one trajectory with persisten…
Robust Fundamental Lemma for Data-driven Control
Jeremy Coulson, Henk van Waarde, Florian Dörfler
The fundamental lemma by Willems and coauthors facilitates a parameterization of all trajectories of a linear time-invariant system in terms of a single, measured one. This result…
Behavioral uncertainty quantification for data-driven control
Alberto Padoan, Jeremy Coulson, Henk J. van Waarde +2
This paper explores the problem of uncertainty quantification in the behavioral setting for data-driven control. Building on classical ideas from robust control, the problem is reg…
Bridging direct & indirect data-driven control formulations via regularizations and relaxations
Florian Dörfler, Jeremy Coulson, Ivan Markovsky
We discuss connections between sequential system identification and control for linear time-invariant systems, often termed indirect data-driven control, as well as a contemporary…
Distributionally Robust Chance Constrained Data-enabled Predictive Control
Jeremy Coulson, John Lygeros, Florian Dörfler
We study the problem of finite-time constrained optimal control of unknown stochastic linear time-invariant systems, which is the key ingredient of a predictive control algorithm -…
Regularized and Distributionally Robust Data-Enabled Predictive Control
Jeremy Coulson, John Lygeros, Florian Dörfler
In this paper, we study a data-enabled predictive control (DeePC) algorithm applied to unknown stochastic linear time-invariant systems. The algorithm uses noise-corrupted input/ou…
Data-Enabled Predictive Control: In the Shallows of the DeePC
Jeremy Coulson, John Lygeros, Florian Dörfler
We consider the problem of optimal trajectory tracking for unknown systems. A novel data-enabled predictive control (DeePC) algorithm is presented that computes optimal and safe co…