no code implementations • 11 Jun 2024 • Valentina Breschi, Chiara Ravazzi, Paolo Frasca, Fabrizio Dabbene, Mara Tanelli
This paper focuses on devising strategies for control-oriented decision-making scenarios, in the presence of social and external influences, e. g. within recommending systems in social contexts.
1 code implementation • 2 May 2024 • Thomas de Jong, Valentina Breschi, Maarten Schoukens, Mircea Lazar
In this paper, we consider the design of data-driven predictive controllers for nonlinear systems from input-output data via linear-in-control input Koopman lifted models.
no code implementations • 21 Mar 2024 • Riccardo Busetto, Valentina Breschi, Federica Baracchi, Simone Formentin
Data-driven control offers a viable option for control scenarios where constructing a system model is expensive or time-consuming.
1 code implementation • 9 Mar 2024 • Per Mattsson, Fabio Bonassi, Valentina Breschi, Thomas B. Schön
Recently, several direct Data-Driven Predictive Control (DDPC) methods have been proposed, advocating the possibility of designing predictive controllers from historical input-output trajectories without the need to identify a model.
no code implementations • 1 Mar 2024 • Aurelio Raffa Ugolini, Valentina Breschi, Andrea Manzoni, Mara Tanelli
In this work we analyze the effectiveness of the Sparse Identification of Nonlinear Dynamics (SINDy) technique on three benchmark datasets for nonlinear identification, to provide a better understanding of its suitability when tackling real dynamical systems.
no code implementations • 30 Jan 2024 • Jessica Leoni, Valentina Breschi, Simone Formentin, Mara Tanelli
Efforts to combine these models often often stumble upon difficulties in finding a balance between accuracy and complexity.
no code implementations • 22 Dec 2023 • Alessandro Chiuso, Marco Fabris, Valentina Breschi, Simone Formentin
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.
no code implementations • 7 Dec 2023 • Riccardo Busetto, Valentina Breschi, Marco Forgione, Dario Piga, Simone Formentin
State estimation has a pivotal role in several applications, including but not limited to advanced control design.
no code implementations • 29 Aug 2023 • Riccardo Busetto, Valentina Breschi, Simone Formentin
Assuming that this supplementary information is available, we propose a novel, direct design approach that leverages the data from similar plants, the knowledge of controllers calibrated on them, and the corresponding closed-loop performance to enhance model-reference control design.
no code implementations • 15 Aug 2023 • Valentina Breschi, Simone Formentin, Alberto Leva
Model Predictive Control (MPC) has proven to be a powerful tool for the control of systems with constraints.
no code implementations • 30 Apr 2023 • 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.
no code implementations • 1 Apr 2023 • Valentina Breschi, Alessandro Chiuso, Marco Fabris, Simone Formentin
Model predictive control (MPC) is a control strategy widely used in industrial applications.
no code implementations • 18 Nov 2022 • Valentina Breschi, Marco Fabris, Simone Formentin, Alessandro Chiuso
Data-Driven Predictive Control (DDPC) has been recently proposed as an effective alternative to traditional Model Predictive Control (MPC), in that the same constrained optimization problem can be addressed without the need to explicitly identify a full model of the plant.
no code implementations • 4 Jul 2022 • 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.
no code implementations • 21 Mar 2022 • 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-efficient and unbiased with respect to the oracle solution.
no code implementations • 22 Oct 2021 • Valentina Breschi, Andrea Sassella, Simone Formentin
The proposed explicit law is build upon a regularized implicit data-driven predictive control problem, so as to guarantee the uniqueness of the explicit predictive controller.
no code implementations • 18 Aug 2021 • Andrea Sassella, Valentina Breschi, Simone Formentin
In this paper, we deal with data-driven predictive control of linear time-invariant (LTI) systems.
no code implementations • 23 Mar 2021 • Valentina Breschi, Claudio De Persis, Simone Formentin, Pietro Tesi
In this work, we introduce a novel data-driven model-reference control design approach for unknown linear systems with fully measurable state.
no code implementations • L4DC 2020 • Valentina Breschi, Simone Formentin
Input saturation is an ubiquitous nonlinearity in control systems and arises from the fact that all actuators are subject to a maximum power, thereby resulting in a hard limitation on the allowable magnitude of the input effort.
no code implementations • L4DC 2020 • Valentina Breschi, Simone Formentin
In control applications where finding a model of the plant is the most costly and time consuming task, Virtual Reference Feedback Tuning (VRFT) represents a valid - purely data-driven - alternative for the design of model reference controllers.