16 citations · 58 across the 23 of their papers we have counts for
24 papers
Direct and Indirect Data-Driven Control with Prior Information about the Equilibrium Manifold
Yi-Chun Liao, Valentina Breschi, Marco M. Nicotra
By hinging on the assumption that a system to be controlled is fully unknown, many data-driven control approaches do not leverage available or readily inferable priors. In contrast…
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, Manuel Klädtke, Chuyu Lu +3
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 th…
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…
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…
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…
On the equivalence of direct and indirect data-driven predictive control approaches
Per Mattsson, Fabio Bonassi, Valentina Breschi +1
Recently, several direct Data-Driven Predictive Control (DDPC) methods have been proposed, advocating the possibility of designing predictive controllers from historical input-outp…
Explainable data-driven modeling via mixture of experts: towards effective blending of grey and black-box models
Jessica Leoni, Valentina Breschi, Simone Formentin +1
Traditional models grounded in first principles often struggle with accuracy as the system's complexity increases. Conversely, machine learning approaches, while powerful, face cha…
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…
Data-driven control of input saturated systems: a LMI-based approach
Federico Porcari, Valentina Breschi, Luca Zaccarian +1
This paper addresses three complex control challenges related to input-saturated systems from a data-driven perspective. Unlike the traditional two-stage process involving system i…
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…
Model predictive control with dynamic move blocking
Valentina Breschi, Simone Formentin, Alberto Leva
Model Predictive Control (MPC) has proven to be a powerful tool for the control of systems with constraints. Nonetheless, in many applications, a major challenge arises, that is fi…
META-SMGO-: similarity as a prior in black-box optimization
Riccardo Busetto, Valentina Breschi, Simone Formentin
When solving global optimization problems in practice, one often ends up repeatedly solving problems that are similar to each others. By providing a rigorous definition of similari…
On the impact of regularization in data-driven predictive control
Valentina Breschi, Alessandro Chiuso, Marco Fabris +1
Model predictive control (MPC) is a control strategy widely used in industrial applications. However, its implementation typically requires a mathematical model of the system being…
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…
Data-driven design of explicit predictive controllers using model-based priors
Valentina Breschi, Andrea Sassella, Simone Formentin
In this paper, we propose a data-driven approach to derive explicit predictive control laws, without requiring any intermediate identification step. The keystone of the presented s…
Data-driven predictive control in a stochastic setting: a unified framework
Valentina Breschi, Alessandro Chiuso, Simone Formentin
Data-driven predictive control (DDPC) has been recently proposed as an effective alternative to traditional model-predictive control (MPC) for its unique features of being time-eff…
On the design of regularized explicit predictive controllers from input-output data
Valentina Breschi, Andrea Sassella, Simone Formentin
On the wave of recent advances in data-driven predictive control, we present an explicit predictive controller that can be constructed from a batch of input/output data only. The p…
Learning explicit predictive controllers: theory and applications
Andrea Sassella, Valentina Breschi, Simone Formentin
In this paper, we deal with data-driven predictive control of linear time-invariant (LTI) systems. Specifically, we show for the first time how explicit predictive laws can be lear…
Direct data-driven model-reference control with Lyapunov stability guarantees
Valentina Breschi, Claudio De Persis, Simone Formentin +1
In this work, we introduce a novel data-driven model-reference control design approach for unknown linear systems with fully measurable state. The proposed control action is compos…