15 citations · 20 across the 12 of their papers we have counts for
10 papers · 1 filter
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…
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…
One controller to rule them all
Riccardo Busetto, Valentina Breschi, Marco Forgione +2
Imagine having a system to control and only know that it belongs to a certain class of dynamical systems. Would it not be amazing to simply plug in a controller and have it work as…
Bias correction and instrumental variables for direct data-driven model-reference control
Manas Mejari, Valentina Breschi, Simone Formentin +1
Managing noisy data is a central challenge in direct data-driven control design. We propose an approach for synthesizing model-reference controllers for linear time-invariant (LTI)…
Meta-learning of data-driven controllers with automatic model reference tuning: theory and experimental case study
Riccardo Busetto, Valentina Breschi, Federica Baracchi +1
Data-driven control offers a viable option for control scenarios where constructing a system model is expensive or time-consuming. Nonetheless, many of these algorithms are not ent…
In-context learning of state estimators
Riccardo Busetto, Valentina Breschi, Marco Forgione +2
State estimation has a pivotal role in several applications, including but not limited to advanced control design. Especially when dealing with nonlinear systems state estimation i…
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…
Meta-learning for model-reference data-driven control
Riccardo Busetto, Valentina Breschi, Simone Formentin
One-shot direct model-reference control design techniques, like the Virtual Reference Feedback Tuning (VRFT) approach, offer time-saving solutions for the calibration of fixed-stru…