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Giancarlo Ferrari Trecate

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Publications associées (78)

Minimal regret state estimation of time-varying systems

Jean-Sébastien Hubert Brouillon, Giancarlo Ferrari Trecate

Kalman and H-infinity filters, the most popular paradigms for linear state estimation, are designed for very specific specific noise and disturbance patterns, which may not appear in practice. State observers based on the minimization of regret measures ar ...

Elsevier2023

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Giancarlo Ferrari Trecate, Clara Lucía Galimberti, Liang Xu, Luca Furieri

Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propose a general class of Hamiltonian DNNs (H-DNNs) that stem from the discretiz ...

2023

Maximum likelihood estimation of distribution grid topology and parameters from Smart Meter data

Jean-Sébastien Hubert Brouillon, Giancarlo Ferrari Trecate

This paper defines a Maximum Likelihood Estimator (MLE) for the admittance matrix estimation of distribution grids, utilising voltage magnitude and power measurements collected only from common, unsychronised measuring devices (Smart Meters). First, we pre ...

IEEE2023

Physically Consistent Neural ODEs for Learning Multi-Physics Systems

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

2023

Safe Zeroth-Order Optimization Using Linear Programs

Baiwei Guo, Giancarlo Ferrari Trecate, Yuning Jiang, Maryam Kamgarpour

To solve unmodeled optimization problems with hard constraints, this paper proposes a novel zeroth-order approach called Safe Zeroth-order Optimization using Linear Programs (SZO-LP). The SZO-LP method solves a linear program in each iteration to find a de ...

New York2023

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

Baiwei Guo, Giancarlo Ferrari Trecate, Yuning Jiang, Maryam Kamgarpour

We address black-box convex optimization problems, where the objective and constraint functions are not explicitly known but can be sampled within the feasible set. The challenge is thus to generate a sequence of feasible points converging towards an optim ...

IEEE2023

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

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

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

Universal Approximation Property of Hamiltonian Deep Neural Networks

Giancarlo Ferrari Trecate, Muhammad Zakwan

This letter investigates the universal approximation capabilities of Hamiltonian Deep Neural Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary Differential Equations. Recently, it has been shown that HDNNs enjoy, by design, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

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

Baiwei Guo, Giancarlo Ferrari Trecate, Luca Furieri, 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

Regularization for distributionally robust state estimation and prediction

Jean-Sébastien Hubert Brouillon, Giancarlo Ferrari Trecate, Florian Dörfler

The increasing availability of sensing techniques provides a great opportunity for engineers to design state estimation methods, which are optimal for the system under observation and the observed noise patterns. However, these patterns often do not fulfil ...

2023

On the Optimality and Convergence Properties of the Iterative Learning Model Predictive Controller

Colin Neil Jones, Yingzhao Lian, Emilio Maddalena, Giancarlo Ferrari Trecate

In this technical article, we analyze the performance improvement and optimality properties of the learning model predictive control (LMPC) strategy for linear deterministic systems. The LMPC framework is a policy iteration scheme where closed-loop traject ...

2023

Data-enabled Predictive Control for Empty Vehicle Rebalancing

Nikolaos Geroliminis, Giancarlo Ferrari Trecate, Pengbo Zhu

A critical operational challenge in Mobility-on-demand systems is the problem of imbalance between vehicle supply and passenger demand. However, conventional model-based methods require accurate parametric system models with complex nonlinear dynamics that ...

IEEE2023

Idle-vehicle Rebalancing Coverage Control for Ride-sourcing systems

Nikolaos Geroliminis, Giancarlo Ferrari Trecate, Pengbo Zhu

Ride-sourcing system can provide passengers with fast and efficient service with a fleet of vehicles, while asymmetry between origin and destination distributions of trips, nonuniform passenger's demand for rides in different districts creates imbalances i ...

IEEE2022

A Unified Passivity-Based Framework for Control of Modular Islanded AC Microgrids

Giancarlo Ferrari Trecate, Pulkit Nahata

Voltage and frequency control in an islanded ac microgrid (ImG) amounts to stabilizing an a priori unknown ImG equilibrium induced by loads and changes in topology. This article puts forth a unified control framework, which, while guaranteeing such stabili ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Finite-sample-based Spectral Radius Estimation and Stabilizability Test for Networked Control Systems

Baiwei Guo, Giancarlo Ferrari Trecate, Liang Xu

In the analysis and control of discrete-time linear time-invariant systems, the spectral radius of the system state matrix plays an essential role. Usually, it is assumed that system matrices are known, from which the spectral radius can be directly comput ...

IEEE2022

Neural Energy Casimir Control for Port-Hamiltonian Systems

Giancarlo Ferrari Trecate, Liang Xu, Muhammad Zakwan

The energy Casimir method is an effective controller design approach to stabilize port-Hamiltonian systems at a desired equilibrium. However, its application relies on the availability of suitable Casimir and Lyapunov functions, whose computation are gener ...

2022

Robust Classification Using Contractive Hamiltonian Neural ODEs

Giancarlo Ferrari Trecate, Liang Xu, Muhammad Zakwan

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

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

Giancarlo Ferrari Trecate, Clara Lucía Galimberti, Luca Furieri

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

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

Empty Vehicle Repositioning for Autonomous Mobility-on-Demand systems via Coverage Control

Nikolaos Geroliminis, Isik Ilber Sirmatel, Giancarlo Ferrari Trecate, Pengbo Zhu

2022

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