activity
20212025
most citedUncertainty-aware data-driven predictive control in a stochastic setting

16 citations · 118 across the 18 of their papers we have counts for

collaborators

18 papers

eess.SY2025

How Complex is a Complex Network? Insights from Linear Systems Theory

Giacomo Baggio, Marco Fabris

This paper leverages linear systems theory to propose a principled measure of complexity for network systems. We focus on a network of first-order scalar linear systems interconnec…

cs.NI2024★ 14 cited

Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques leveraging Network Observability

Marco Fabris, Riccardo Ceccato, Andrea Zanella

The emergence of 6G-enabled Internet of Vehicles (IoV) promises to revolutionize mobility and connectivity, integrating vehicles into a mobile Internet of Things (IoT)-oriented wir…

eess.SY2023★ 15 cited

Harnessing Uncertainty for a Separation Principle in Direct Data-Driven Predictive Control

Alessandro Chiuso, Marco Fabris, Valentina Breschi +1

Model Predictive Control (MPC) is a powerful method for complex system regulation, but its reliance on an accurate model poses many limitations in real-world applications. Data-dri…

eess.SY2023★ 7 cited

Optimal Time-Invariant Distributed Formation Tracking for Second-Order Multi-Agent Systems

Marco Fabris, Giulio Fattore, Angelo Cenedese

This paper addresses the optimal time-invariant formation tracking problem with the aim of providing a distributed solution for multi-agent systems with second-order integrator dyn…

eess.SY2023★ 6 cited

Adaptive Consensus-based Reference Generation for the Regulation of Open-Channel Networks

Marco Fabris, Marco D. Bellinazzi, Andrea Furlanetto +1

This paper deals with water management over open-channel networks (OCNs) subject to water height imbalance. The OCN is modeled by means of graph theoretic tools and a regulation sc…

eess.SY2023★ 6 cited

A Robustness Analysis to Structured Channel Tampering Over Secure-by-Design Consensus Networks

Marco Fabris, Daniel Zelazo

This work addresses multi-agent consensus networks where adverse attackers affect the convergence performances of the protocol by manipulating the edge weights. We generalize (Fabr…

eess.SY2023

On the Characterization of Regular Ring Lattices and their Relation with the Dirichlet Kernel

Marco Fabris

Regular ring lattices (RRLs) are defined as peculiar undirected circulant graphs constructed from a cycle graph, wherein each node is connected to pairs of neighbors that are space…

eess.SY2023★ 11 cited

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…

eess.SY2022★ 16 cited

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…

eess.SY2022★ 2 cited

Distributed Dual Quaternion Based Localization of Visual Sensor Networks

Luca Varotto, Marco Fabris, Giulia Michieletto +1

In this paper we consider the localization problem for a visual sensor network. Inspired by the alternate attitude and position distributed optimization framework discussed in [1],…

eess.SY2022★ 5 cited

On the Distributed Estimation from Relative Measurements: a Graph-Based Convergence Analysis

Marco Fabris, Giulia Michieletto, Angelo Cenedese

For a multi-agent system state estimation resting upon noisy measurements constitutes a problem related to several application scenarios. Adopting the standard least-squares approa…

eess.SY2022★ 8 cited

Optimal Time-Invariant Formation Tracking for a Second-Order Multi-Agent System

Marco Fabris, Angelo Cenedese, John Hauser

Given a multi-agent linear system, we formalize and solve a trajectory optimization problem that encapsulates trajectory tracking, distance-based formation control and input energy…

eess.SY2022★ 4 cited

Distributed Strategies for Dynamic Coverage with Limited Sensing Capabilities

Marco Fabris, Angelo Cenedese

In this work, it is presented the development of a novel distributed algorithm performing robotic coverage, clustering and dispatch around an event in static-obstacle structured en…

eess.SY2022★ 3 cited

Bearing-based Autonomous Communication Relay Positioning under Field-of-View Constraints

Marco Fabris, Daniel Zelazo

This paper investigates the problem of communication relay establishment for multiple agent-based mobile units using a relay vehicle. The objective is to drive autonomously the rel…

eess.SP2022★ 8 cited

Visual Sensor Network Stimulation Model Identification via Gaussian Mixture Model and Deep Embedded Features

Luca Varotto, Marco Fabris, Giulia Michieletto +1

Visual sensor networks (VSNs) constitute a fundamental class of distributed sensing systems, with unique complexity and appealing performance features, which correspondingly bring…

eess.SY2021

A General Regularized Distributed Solution for System State Estimation from Relative Measurements

Marco Fabris, Giulia Michieletto, Angelo Cenedese

This work presents a novel general regularized distributed solution for the state estimation problem in networked systems. Resting on the graph-based representation of sensor netwo…

eess.SY2021★ 9 cited

Secure Consensus via Objective Coding: Robustness Analysis to Channel Tampering

Marco Fabris, Daniel Zelazo

This work mainly addresses continuous-time multiagent consensus networks where an adverse attacker affects the convergence performances of said protocol. In particular, we develop…

eess.SY2021★ 4 cited

A Proximal Point Approach for Distributed System State Estimation

Marco Fabris, Giulia Michieletto, Angelo Cenedese

System state estimation constitutes a key problem in several applications involving multi-agent system architectures. This rests upon the estimation of the state of each agent in t…