Search Results for author: Valentina Breschi

Found 20 papers, 2 papers with code

Optimal policy design for decision problems under social influence

no code implementations11 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.

Decision Making

Koopman Data-Driven Predictive Control with Robust Stability and Recursive Feasibility Guarantees

1 code implementation2 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.

Meta-learning of data-driven controllers with automatic model reference tuning: theory and experimental case study

no code implementations21 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.

Meta-Learning

On the equivalence of direct and indirect data-driven predictive control approaches

1 code implementation9 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.

SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study

no code implementations1 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.

Benchmarking

Explainable data-driven modeling via mixture of experts: towards effective blending of grey and black-box models

no code implementations30 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.

Harnessing Uncertainty for a Separation Principle in Direct Data-Driven Predictive Control

no code implementations22 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.

Model Predictive Control valid

In-context learning of state estimators

no code implementations7 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.

In-Context Learning

Meta-learning for model-reference data-driven control

no code implementations29 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.

Meta-Learning Philosophy

Model predictive control with dynamic move blocking

no code implementations15 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.

Blocking Model Predictive Control

META-SMGO-$Δ$: similarity as a prior in black-box optimization

no code implementations30 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.

Meta-Learning

Uncertainty-aware data-driven predictive control in a stochastic setting

no code implementations18 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.

Model Predictive Control

Data-driven design of explicit predictive controllers using model-based priors

no code implementations4 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.

Data-driven predictive control in a stochastic setting: a unified framework

no code implementations21 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.

Model Predictive Control

On the design of regularized explicit predictive controllers from input-output data

no code implementations22 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.

Learning explicit predictive controllers: theory and applications

no code implementations18 Aug 2021 Andrea Sassella, Valentina Breschi, Simone Formentin

In this paper, we deal with data-driven predictive control of linear time-invariant (LTI) systems.

LEMMA

Direct data-driven model-reference control with Lyapunov stability guarantees

no code implementations23 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.

Direct data-driven control with embedded anti-windup compensation

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.

Virtual Reference Feedback Tuning with data-driven reference model selection

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.

Model Selection valid

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