13 citations · 17 across the 13 of their papers we have counts for
19 papers
Minimal regret state estimation of time-varying systems
Jean-Sébastien Brouillon, Florian Dörfler, Giancarlo Ferrari-Trecate
Kalman and H-infinity filters, the most popular paradigms for linear state estimation, are designed for very specific specific noise and disturbance patterns, which may not appear…
Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control
Andrea Martin, Luca Furieri, Florian Dörfler +2
We consider control of dynamical systems through the lens of competitive analysis. Most prior work in this area focuses on minimizing regret, that is, the loss relative to an ideal…
Physically Consistent Neural ODEs for Learning Multi-Physics Systems
Muhammad Zakwan, Loris Di Natale, Bratislav Svetozarevic +3
Despite the immense success of neural networks in modeling system dynamics from data, they often remain physics-agnostic black boxes. In the particular case of physical systems, th…
Maximum likelihood estimation of distribution grid topology and parameters from smart meter data
Lisa Laurent, Jean-Sébastien Brouillon, Giancarlo Ferrari-Trecate
This paper defines a Maximum Likelihood Estimator (MLE) for the admittance matrix estimation of distribution grids, utilising voltage magnitude and power measurements collected onl…
Optimal droop control placement in distribution network via an exact OPF relaxation method
H. Sekhavatmanesh, G. Ferrari-Trecate, S. Mastellone
In the last decade, the integration of Renewable Energy Sources (RES) in distribution networks has been constantly increasing due to their many technical, economical, and environme…
Robust online joint state/input/parameter estimation of linear systems
Jean-Sébastien Brouillon, Keith Moffat, Florian Dörfler +1
This paper presents a method for jointly estimating the state, input, and parameters of linear systems in an online fashion. The method is specially designed for measurements that…