From the 1 of 5 linked papers with an AI index.
5 papers
A subspace approach to data-driven predictive control for linear parameter-varying systems
Federico Porcari, Chris Verhoek, Valentina Breschi +2
The paper proposes a subspace-based data‑driven predictive control scheme for linear parameter‑varying (LPV) systems that avoids explicit model identification and offers reduced co…
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…
Harnessing Uncertainty for a Separation Principle in Direct Data-Driven Predictive Control
Alessandro Chiuso, Marco Fabris, Valentina Breschi +1
Model Predictive Control (MPC) is a powerful method for complex system regulation, but its reliance on an accurate model poses many limitations in real-world applications. Data-dri…