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Giancarlo Ferrari Trecate

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Publications associées (78)

Consensus-Based Current Sharing and Voltage Balancing in DC Microgrids with Exponential Loads

Giancarlo Ferrari Trecate, Pulkit Nahata, Mustafa Sahin Turan

2022

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

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

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

Data-Driven Unknown-Input Observers and State Estimation

Giancarlo Ferrari Trecate, Mustafa Sahin Turan

Unknown-input observers (UIOs) allow for estimation of the states of an LTI system without knowledge of all inputs. In this letter, we provide a novel data-driven UIO based on behavioral system theory and the result known as Fundamental Lemma proposed by J ...

2022

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

Optimal Adaptive Droop Design via a Modified Relaxation of the OPF

Giancarlo Ferrari Trecate

The ever-increasing penetration of renewable energy resources (RESs) in power distribution networks has brought, among others, the challenge of maintaining the grid voltages within the secure region. Employing droop voltage regulators on the RES's inverter ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Learning Robustly Safe Output Feedback Controllers from Noisy Data with Performance Guarantees

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

How can we synthesize a safe and near-optimal control policy for a partially-observed system, if all we are given is one historical input/output trajectory that has been corrupted by noise? To address this challenge, we suggest a novel data-driven controll ...

2021

Bayesian Methods for the Identification of Distribution Networks

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

The increasing integration of intermittent renewable generation, especially at the distribution level, necessitates advanced planning and optimisation methodologies contingent on the knowledge of the admittance matrix, capturing the topology and line param ...

IEEE2021

Supervised model predictive control of large-scale electricity networks via clustering methods

Giancarlo Ferrari Trecate

This article describes a control approach for large-scale electricity networks, with the goal of efficiently coordinating distributed generators to balance unexpected load variations with respect to nominal forecasts. To mitigate the difficulties due to th ...

2021

A unified framework for Hamiltonian deep neural networks

Giancarlo Ferrari Trecate, Clara Lucía Galimberti, Liang Xu

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

Actuator Placement for Structural Controllability beyond Strong Connectivity and towards Robustness

Baiwei Guo, Giancarlo Ferrari Trecate, Maryam Kamgarpour

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

A Behavioral Input-Output Parametrization of Control Policies with Suboptimality Guarantees

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

Recent work in data-driven control has revived behavioral theory to perform a variety of complex control tasks, by directly plugging libraries of past input-output trajectories into optimal control problems. Despite recent advances, a key aspect remains un ...

IEEE2021

On Consensusability of Linear Interconnected Multi-Agent Systems and Simultaneous Stabilization

Giancarlo Ferrari Trecate, Mustafa Sahin Turan, Liang Xu

Consensusability of multi-agent systems (MASs) certifies the existence of a distributed controller capable of driving the states of each subsystem to a consensus value. We study the consensusability of linear interconnected MASs (LIMASs) where, as in sever ...

2021

A Distributed Cyber-Attack Detection Scheme With Application to DC Microgrids

Giancarlo Ferrari Trecate, Mustafa Sahin Turan

DC microgrids often present a hierarchical control architecture, requiring integration of communication layers. This leads to the possibility of malicious attackers disrupting the overall system. Motivated by this application, in this article, we present a ...

2020

Suboptimal Distributed LQR Design for Physically Coupled Systems

Baiwei Guo, Giancarlo Ferrari Trecate, Clara Lucía Galimberti, Liang Xu

In this paper, we propose a suboptimal distributed LQR control method, applicable to systems coupled through both physical interconnections and the quadratic cost to be minimized. Thanks to a novel suboptimal but distributed cost-to-go matrix update that e ...

ELSEVIER2020

A Passivity-Based Approach to Voltage Stabilization in DC Microgrids with ZIP loads

Giancarlo Ferrari Trecate, Michele Tucci, Pulkit Nahata, Raffaele Soloperto, Andrea Martinelli

We consider the application of passivity theory to the problem of voltage stabilization in DC microgrids, which are composed of distributed generation units, dynamic RLC lines, and nonlinear ZIP (constant impedance, constant current, and constant power) lo ...

2020

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

Consensusability of linear interconnected multi-agent systems

Giancarlo Ferrari Trecate, Mustafa Sahin Turan, Liang Xu

Consensusability is an important property for many multi-agent systems (MASs) as it implies the existence of networked controllers driving the states of MAS subsystems to the same value. Consensusability is of interest even when the MAS subsystems are phys ...

ELSEVIER2020

Hierarchical Control in Islanded DC Microgrids With Flexible Structures

Alessio La Bella, 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

Approximate Kron Reduction Methods for Electrical Networks With Applications to Plug-and-Play Control of AC Islanded Microgrids

Giancarlo Ferrari Trecate, Michele Tucci, Alessandro Floriduz

Kron reduction (KR) is a methodology for analyzing an electrical network by replacing it with a simpler circuit having less nodes but the same terminal behavior of voltages and currents at target vertices. Existing approaches to instantaneous KR, however, ...

2019

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