From the 1 of 8 linked papers with an AI index.
8 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…
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…
Uncertainty-aware data-driven predictive control in a stochastic setting
Valentina Breschi, Marco Fabris, Simone Formentin +1
Data-Driven Predictive Control (DDPC) has been recently proposed as an effective alternative to traditional Model Predictive Control (MPC), in that the same constrained optimizatio…
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…