140 citations · 338 across the 22 of their papers we have counts for
23 papers
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…
Experience in Engineering Complex Systems: Active Preference Learning with Multiple Outcomes and Certainty Levels
Le Anh Dao, Loris Roveda, Marco Maccarini +8
Black-box optimization refers to the optimization problem whose objective function and/or constraint sets are either unknown, inaccessible, or non-existent. In many applications, e…
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…
Hourly operation of a regulated lake via Model Predictive Control
Raffaele G. Cestari, Andrea Castelletti, Simone Formentin
The optimal operation of regulated lakes is a challenging task involving conflicting objectives, ranging from controlling lake levels to avoid floods and low levels to water supply…
The Twin-in-the-Loop approach for vehicle dynamics control
Federico Dettù, Simone Formentin, Sergio Matteo Savaresi
In vehicle dynamics control, engineering a suitable regulator is a long and costly process. The starting point is usually the design of a nominal controller based on a simple contr…
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…
Twin-in-the-loop state estimation for vehicle dynamics control: theory and experiments
Giorgio Riva, Simone Formentin, Matteo Corno +1
In vehicle dynamics control, many variables of interest cannot be directly measured, as sensors might be costly, fragile, or even not available. Therefore, real-time estimation tec…
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…
Learning-based hierarchical control of water reservoir systems
Pauline Kergus, Simone Formentin, Matteo Giuliani +1
The optimal control of a water reservoir systems represents a challenging problem, due to uncertain hydrologic inputs and the need to adapt to changing environment and varying cont…
Experimental Automatic Calibration of a Semi-Active Suspension Controller via Bayesian Optimization
Gianluca Savaia, Youngil Sohn, Simone Formentin +3
The End-of-Line (EoL) calibration of semi-active suspension systems for road vehicles is usually a critical and expensive task, needing a team of vehicle and control experts as wel…
Performance-oriented model learning for data-driven MPC design
Dario Piga, Marco Forgione, Simone Formentin +1
Model Predictive Control (MPC) is an enabling technology in applications requiring controlling physical processes in an optimized way under constraints on inputs and outputs. Howev…
Data-Driven Inversion-Based Control: closed-loop stability analysis for MIMO systems
Carlo Novara, Simone Formentin
Data-Driven Inversion-Based Control (D-IBC) is a recently introduced control design method for uncertain nonlinear systems, relying on a two degree-of-freedom architecture, w…
Online wheel speed filtering for periodic disturbance reduction: a strategy for an advanced bicycle application
Gianmarco Rallo, Simone Formentin, Sergio Matteo Savaresi
Due to geometrical errors and possible misalignment of the sensors, wheel speed measurements provided by incremental encoders in road vehicles are usually affected by significant p…
Direct data-driven control of constrained linear parameter-varying systems: A hierarchical approach
Dario Piga, Simone Formentin, Alberto Bemporad
In many nonlinear control problems, the plant can be accurately described by a linear model whose operating point depends on some measurable variables, called scheduling signals. W…
Least costly energy management for series hybrid electric vehicles
Simone Formentin, Jacopo Guanetti, Sergio M. Savaresi
Energy management of plug-in Hybrid Electric Vehicles (HEVs) has different challenges from non-plug-in HEVs, due to bigger batteries and grid recharging. Instead of tackling it to…
Robust linear static anti-windup with probabilistic certificates
Simone Formentin, Fabrizio Dabbene, Roberto Tempo +2
In this paper, we address robust static anti-windup compensator design and performance analysis for saturated linear closed loops in the presence of nonlinear probabilistic paramet…
Data-driven controller design for nonlinear systems: a two degrees of freedom architecture
Carlo Novara, Simone Formentin
In this paper, the D2-IBC (Data-Driven Inversion Based Control) approach for nonlinear control is introduced and analyzed. The method does not require any a-priori knowledge of the…
Scenario optimization with certificates and applications to anti-windup design
Simone Formentin, Fabrizio Dabbene, Roberto Tempo +2
In this paper, we introduce a significant extension, called scenario with certificates (SwC), of the so-called scenario approach for uncertain optimization problems. This extension…