4 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…
An Extended Kalman Filter for Data-enabled Predictive Control
Daniele Alpago, Florian Dorfler, John Lygeros
The literature dealing with data-driven analysis and control problems has significantly grown in the recent years. Most of the recent literature deals with linear time-invariant sy…
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…