1 citations · 1 across the 9 of their papers we have counts for
10 papers
A subspace approach to data-driven predictive control for linear parameter-varying systems
Federico Porcari, Chris Verhoek, Valentina Breschi +2
This paper presents a subspace data-driven predictive control method for linear parameter-varying (LPV) systems. Starting from an affine LPV state-space model in innovation form, w…
When Persistency is not Exciting in Data-Driven Predictive Control
Gianluca Giacomelli, Chuyu Lu, Siep Weiland +1
Understanding how to collect data that is meaningful for control purposes is of paramount importance in data-driven control. While existing approaches have primarily relied on the…
Beyond Shrinkage: Foundations of Data-Driven Control for Piecewise Affine Systems
Gianluca Giacomelli, Victor G. Lopez, Simone Formentin +2
Data-enabled predictive control (DeePC) has recently attracted attention as a promising approach for controlling systems directly from raw data, without requiring an explicit ident…
Toward Federated DeePC: borrowing data from similar systems
Gert Vankan, Valentina Breschi, Simone Formentin
Data-driven predictive control approaches, in general, and Data-enabled Predictive Control (DeePC), in particular, exploit matrices of raw input/output trajectories for control des…
Insights into the explainability of Lasso-based DeePC for nonlinear systems
Gianluca Giacomelli, Simone Formentin, Victor G. Lopez +2
Data-enabled Predictive Control (DeePC) has recently gained the spotlight as an easy-to-use control technique that allows for constraint handling while relying on raw data only. In…
In-Context Learning for Zero-Shot Speed Estimation of BLDC motors
Alessandro Colombo, Riccardo Busetto, Valentina Breschi +3
Accurate speed estimation in sensorless brushless DC motors is essential for high-performance control and monitoring, yet conventional model-based approaches struggle with system n…