Publications by Giancarlo Ferrari Trecate

Related publications (75)

Identification of AC Distribution Networks With Recursive Least Squares and Optimal Design of Experiment

Giancarlo Ferrari Trecate, Pulkit Nahata

The increasing penetration of intermittent distributed energy resources in power networks calls for novel planning and control methodologies which hinge on detailed knowledge of the grid. However, reliable information concerning the system topology and par ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Bayesian Error-in-Variables Models for the Identification of Distribution Grids

Giancarlo Ferrari Trecate, Pulkit Nahata, Jean-Sébastien Hubert Brouillon

The increasing integration of renewable energy requires a good model of the existing power distribution infrastructure, represented by its admittance matrix. However, a reliable estimate may either be missing or quickly become obsolete, as distribution gri ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

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

Robust Classification Using Contractive Hamiltonian Neural ODEs

Giancarlo Ferrari Trecate, Muhammad Zakwan, Liang Xu

Deep neural networks can be fragile and sensitive to small input perturbations that might cause a significant change in the output. In this letter, we employ contraction theory to improve the robustness of neural ODEs (NODEs). A dynamical system is contrac ...

2022

Actuator Placement for Structural Controllability beyond Strong Connectivity and towards Robustness

Giancarlo Ferrari Trecate, Maryam Kamgarpour, Baiwei Guo

Actuator placement is a fundamental problem in control design for large-scale networks. In this paper, we study the problem of finding a set of actuator positions by minimizing a given metric, while satisfying a structural controllability requirement and a ...

IEEE2021

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

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti

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

Optimal control configuration in distribution network via an exact OPF relaxation method

Giancarlo Ferrari Trecate

In the last decade, the integration of Renewable Energy Sources (RESs) in distribution networks has been constantly increasing due to their many technical, economical, and environmental benefits. However, the large-scale penetration of RESs is limited by t ...

IEEE2022

Distributed Fault Detection for Interconnected Large-Scale Systems: A Scalable Plug & Play Approach

Giancarlo Ferrari Trecate

In this paper, we propose a novel distributed fault detection method to monitor the state of a-possibly large scale-linear system, partitioned into interconnected subsystems. The approach hinges on the definition of a partition-based distributed Luenberger ...

2019

Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti

We address the problem of designing stabilizing control policies for nonlinear systems in discrete-time, while minimizing an arbitrary cost function. When the system is linear and the cost is convex, the System Level Synthesis (SLS) approach offers an effe ...

IEEE2022

A PAC-Bayesian Framework for Optimal Control with Stability Guarantees

Mahrokh Ghoddousiboroujeni, 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

A Supervisory Control Structure for Voltage-Controlled Islanded DC Microgrids

Giancarlo Ferrari Trecate, Pulkit Nahata

In this work, we propose a supervisory control structure in islanded DC microgrids such that a well scheduled and balanced utilization of various resources is achieved. Our supervisory control layer rests on top of a voltage-controlled primary layer and co ...

2019

Near-Optimal Design of Safe Output-Feedback Controllers From Noisy Data

Giancarlo Ferrari Trecate, Luca Furieri, Baiwei Guo, Andrea Martin

As we transition toward the deployment of data-driven controllers for black-box cyberphysical systems, complying with hard safety constraints becomes a primary concern. Two key aspects should be addressed when input-output data are corrupted by noise: how ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach

Giancarlo Ferrari Trecate, Luca Furieri, Muhammad Zakwan, Clara Lucía Galimberti

Large-scale cyber-physical systems require that control policies are distributed, that is, that they only rely on local real-time measurements and communication with neighboring agents. Optimal Distributed Control (ODC) problems are, however, highly intrac ...

2022

A scalable, line-independent control design algorithm for voltage and frequency stabilization in AC islanded microgrids

Giancarlo Ferrari Trecate, Michele Tucci

We propose a decentralized control synthesis procedure for stabilizing voltage and frequency in AC Islanded microGrids (ImGs) composed of Distributed Generation Units (DGUs) and loads interconnected through power lines. The presented approach enables Plug- ...

2020

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

Hierarchical Control in Islanded DC Microgrids With Flexible Structures

Giancarlo Ferrari Trecate, Pulkit Nahata

Hierarchical architectures stacking primary, secondary, and tertiary layers are widely employed for the operation and control of islanded dc microgrids (dcmGs) composed of distribution generation units (DGUs), loads, and power lines. However, a comprehensi ...

2020

On Existence of Equilibria, Voltage Balancing, and Current Sharing in Consensus-Based DC Microgrids

Giancarlo Ferrari Trecate, Pulkit Nahata

This work presents new secondary regulators for current sharing and voltage balancing in DC microgrids, composed of distributed generation units, dynamic RLC lines, and nonlinear ZIP (constant impedance, constant current, and constant power) loads. The pro ...

IEEE2020

Learning to Boost the Performance of Stable Nonlinear Systems

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti

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

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 unified framework for Hamiltonian deep neural networks

Giancarlo Ferrari Trecate, Liang Xu, Clara Lucía Galimberti

Training deep neural networks (DNNs) can be difficult due to the occurrence of vanishing/explod- ing gradients during weight optimization. To avoid this problem, we propose a class of DNNs stemming from the time discretization of Hamiltonian systems. The t ...

2021