Publications de Giancarlo Ferrari Trecate

Parametrizations of all stable closed-loop responses: From theory to neural network control design

Giancarlo Ferrari Trecate, Clara Lucía Galimberti, Luca Furieri

The complexity of modern control systems necessitates architectures that achieve high performance while ensuring robust stability, particularly for nonlinear systems. In this work, we tackle the challenge of designing output-feedback controllers to boost t ...

2025

Erratum to “Learning to Boost the Performance of Stable Nonlinear Systems”

Giancarlo Ferrari Trecate, Clara Lucía Galimberti, Luca Furieri

Institute of Electrical and Electronics Engineers (IEEE)2025

On the Guarantees of Minimizing Regret in Receding Horizon

Giancarlo Ferrari Trecate, Luca Furieri, Andrea Martin

Toward bridging classical optimal control and online learning, regret minimization has recently been proposed as a control design criterion. This competitive paradigm penalizes the loss relative to the optimal control actions chosen by a clairvoyant policy ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

Distributionally robust infinite-horizon control: from a pool of samples to the design of dependable controllers

Jean-Sébastien Hubert Brouillon, Giancarlo Ferrari Trecate, Andrea Martin

We study control of constrained linear systems with only partial statistical information about the uncertainty affecting the system dynamics and the sensor measurements. Specifically, given a finite collection of disturbance realizations drawn from a gener ...

Institute of Electrical and Electronics Engineers (IEEE)2025

Power grid parameter estimation without phase measurements: Theory and empirical validation

Jean-Sébastien Hubert Brouillon, Giancarlo Ferrari Trecate

Reliable integration and operation of renewable distributed energy resources requires accurate distribution grid models. However, obtaining precise models by field inspection is often prohibitively expensive, given their large scale and the ongoing nature ...

Elsevier2024

Learning to Boost the Performance of Stable Nonlinear Systems

Giancarlo Ferrari Trecate, Clara Lucía Galimberti, Luca Furieri

The growing scale and complexity of safety-critical control systems underscore the need to evolve current control architectures aiming for the unparalleled performances achievable through state-of-the-art optimization and machine learning algorithms. Howev ...

2024

Neural Exponential Stabilization of Control-affine Nonlinear Systems

Giancarlo Ferrari Trecate, Muhammad Zakwan

This paper proposes a novel learning-based approach for achieving exponential stabilization of nonlinear control-affine systems. We leverage the Control Contraction Metrics (CCMs) framework to co-synthesize Neural Contraction Metrics (NCMs) and Neural Netw ...

Institute of Electrical and Electronics Engineers Inc.2024

Optimal distributed control with stability guarantees by training a network of neural closed-loop maps

Giancarlo Ferrari Trecate, Luca Furieri, Danilo Saccani, Leonardo Massai

This paper proposes a novel approach to improve the performance of distributed nonlinear control systems while preserving stability by leveraging Deep Neural Networks (DNNs). We build upon the Neural System Level Synthesis (Neur-SLS) framework and introduc ...

Institute of Electrical and Electronics Engineers Inc.2024

SIMBa: System Identification Methods Leveraging Backpropagation

Colin Neil Jones, Loris Di Natale, Giancarlo Ferrari Trecate, Muhammad Zakwan

This manuscript details and extends the system identification methods leveraging the backpropagation (SIMBa) toolbox presented in previous work, which uses well-established machine learning tools for discrete-time linear multistep-ahead state-space system ...

2024

Stable Linear Subspace Identification: A Machine Learning Approach

Colin Neil Jones, Giancarlo Ferrari Trecate

Machine Learning (ML) and linear System Identification (SI) have been historically developed independently. In this paper, we leverage well-established ML tools - especially the automatic differentiation framework - to introduce SIMBa, a family of discrete ...

Institute of Electrical and Electronics Engineers Inc.2024

Closing the Gap to Quadratic Invariance: A Regret Minimization Approach to Optimal Distributed Control

Giancarlo Ferrari Trecate, Luca Furieri, Daniele Martinelli

In this work, we focus on the design of optimal controllers that must comply with an information structure. State-of-the-art approaches do so based on the H2 or H∞ norm to minimize the expected or worst-case cost in the presence of stochastic or adversaria ...

IEEE2024

A Coverage Control-Based Idle Vehicle Rebalancing Approach for Autonomous Mobility-on-Demand Systems

Nikolaos Geroliminis, Giancarlo Ferrari Trecate, Pengbo Zhu

As an emerging mode of urban transportation, autonomous mobility-on-demand (AMoD) systems show the potential in improving mobility in cities through timely and door-to-door services. However, the spatiotemporal imbalances between mobility demand and supply ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Unconstrained Learning of Networked Nonlinear Systems via Free Parametrization of Stable Interconnected Operators

Giancarlo Ferrari Trecate, Luca Furieri, Danilo Saccani, Leonardo Massai

This paper characterizes a new parametrization of nonlinear networked incrementally L2 -bounded operators in discrete time. The distinctive novelty is that our parametrization is free - that is, a sparse large-scale operator with bounded incremental L2 gai ...

Institute of Electrical and Electronics Engineers Inc.2024

Neural Distributed Controllers with Port-Hamiltonian Structures

Giancarlo Ferrari Trecate, Muhammad Zakwan

Controlling large-scale cyber-physical systems necessitates optimal distributed policies, relying solely on local real-time data and limited communication with neighboring agents. However, finding optimal controllers remains challenging, even in seemingly ...

Institute of Electrical and Electronics Engineers Inc.2024

A PAC-Bayesian Framework for Optimal Control with Stability Guarantees

Giancarlo Ferrari Trecate, Clara Lucía Galimberti

Stochastic Nonlinear Optimal Control (SNOC) involves minimizing a cost function that averages out the random uncertainties affecting the dynamics of nonlinear systems. For tractability reasons, this problem is typically addressed by minimizing an empirical ...

IEEE2024

Hierarchical Control for Vehicle Repositioning in Autonomous Mobility-on-Demand Systems

Nikolaos Geroliminis, Giancarlo Ferrari Trecate, Pengbo Zhu

Balancing passenger demand and vehicle availability is crucial for ensuring the sustainability and effectiveness of urban transportation systems. To address this challenge, we propose a novel hierarchical strategy for the efficient distribution of empty ve ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

Regret optimal control for uncertain stochastic systems

Giancarlo Ferrari Trecate, Luca Furieri, Andrea Martin

We consider control of uncertain linear time-varying stochastic systems from the perspective of regret minimization. Specifically, we focus on the problem of designing a feedback controller that minimizes the loss relative to a clairvoyant optimal policy t ...

ELSEVIER2024

Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control

John Lygeros, Giancarlo Ferrari Trecate, Luca Furieri, Andrea Martin, Florian Dörfler

We consider control of dynamical systems through the lens of competitive analysis. Most prior work in this area focuses on minimizing regret, that is, the loss relative to an ideal clairvoyant policy that has noncausal access to past, present, and future d ...

Elsevier2023

Homothetic Tube Model Predictive Control With Multi-Step Predictors

Melanie Nicole Zeilinger, Giancarlo Ferrari Trecate, Danilo Saccani

We present a robust model predictive control (MPC) framework for linear systems facing bounded parametric uncertainty and bounded disturbances. Our approach deviates from standard MPC formulations by integrating multi-step predictors, which provide reduced ...

Ieee-Inst Electrical Electronics Engineers Inc2023

Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations

Giancarlo Ferrari Trecate, Clara Lucía Galimberti, Luca Furieri, Daniele Martinelli

In this work, we introduce and study a class of Deep Neural Networks (DNNs) in continuous-time. The proposed architecture stems from the combination of Neural Ordinary Differential Equations (Neural ODEs) with the model structure of recently introduced Rec ...

IEEE2023