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Rubrica
Qualifica
Ricercatore a tempo det. art. 24 c. 3 lett. A L. 240/2010
Indirizzo
VIA G. GRADENIGO, 6/B - PADOVA
Telefono
Marco Fabris received the Laurea (M.Sc.) degree (with honors) in Automation Engineering and his Ph.D. both from the University of Padua (UniPD), in 2016 and 2020, respectively. In 2018, he spent six months at the University of Colorado Boulder, USA, as a visiting scholar (funded by the awarded grant "Ing. Aldo Gini") focusing on optimal time-invariant formation tracking. In 2020-2021, he was post-doctoral fellow at the Philadelphia Flight Control Lab. in the Faculty of Aerospace Engineering, Technion - Israel Institute of Technology, Haifa. There, he was in charge to work on the project "Development of a secure-by-design approach for design and analysis of consensus networks under attack". From January 2022 to July 2023 he was post-doctoral fellow at UniPD and he focused on the projects "Preliminary study for the development of flow estimation and control algorithms in a water-channel network", "Water-channel network modeling, estimation, and control" and "Development and analysis of data-driven control methods". He is currently research fellow (RTD-A) with the Telecommunications group at the Department of Information Engineering (DEI), UniPD and he is now involved in work packages 3 and 4 of the spoke 8 "MaaS and Innovative Services", under the Centro Nazionale per la Mobilità Sostenibile (MOST).
His main research interests involve graph-based consensus theory, analysis and control resilient networks, optimal decentralized control and estimation for networked systems, data-driven predictive control and optimization strategies for mobility as a service.
Dr. Fabris fluently speaks Italian, English and Portuguese. In 2010 he spent a few months in an internship with Serai S.p.a., Legnaro (PD), where he worked as a technician on electronic devices. From 2017 to 2020, he has been teaching assistant for the courses of Calculus 1 (Analisi 1) and Networked Control for Multi-agent Systems at DEI, UniPD. He has also been co-advisor of 3 master theses at DEI, UniPD and co-supervisor of an undergraduate student at the Faculty of Aerospace Engineering, Technion.
Dr. Fabris has been co-author of more than 15 scientific papers. He is currently member of the IEEE community, the Centro studi di economia e tecnica dell'energia Giorgio Levi Cases, the Space, Aerial and Ground Control Systems (SPARCS) and the Artificial Intelligence, Machine learning and Control (AMCO). He was chair for the "Autonomous System I" session at the 18th European Control Conference in Naples. He has served as a reviewer for several journals and congresses, such as Transactions on Automatic Control, Control Systems Letters, Transaction on Industrial Informatics, Robotics and Automation Letters, American Control Conference, Mediterranean Conference on Control and Automation (MED), International Conference on Decision and Control, European Control Conference (ECC), International Conference on Robotics and Automation, International Conference on Intelligent Robots and Systems, IFAC World Congress, Neurocomputing, International Journal of Robust and Nonlinear Control. He has delivered invited seminars and conference talks at the Control Days, Israeli Association for Automatic Control, ECC, MED and IFAC World Congress.
Avvisi
Orari di ricevimento
by appointment via e-mail [marco.fabris.1@unipd.it] or telephone number [+39 0498277705].
Pubblicazioni
Google Scholar profile:
https://scholar.google.it/citations?user=rwBitYYAAAAJ&hl=it
See also the attached CV in the tab of Curriculum Vitae for more details.
List of the most relevant publications:
• "Optimal Time-Invariant Distributed Formation Tracking for Second-Order Multi-Agent Systems", by M. Fabris, G. Fattore, A. Cenedese, in European Journal of Control, 2024
• "Adaptive Consensus-based Reference Generation for the Regulation of Open-Channel Networks", by M. Fabris, M. D. Bellinazzi, A. Furlanetto, A. Cenedese, in IEEE Accesss, 2024
• "A robustness analysis to structured channel tampering over secure-by-design consensus networks", by M. Fabris, D. Zelazo, in IEEE Control Systems Letters, 2023
• "Secure Consensus via Objective Coding: Robustness Analysis to Channel Tampering", by M. Fabris, D. Zelazo, in IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022
• "A general regularized distributed solution for system state estimation from relative measurements", by M. Fabris, G. Michieletto, A. Cenedese, in IEEE Control Systems Letters, 2021
Area di ricerca
KEYWORDS
> Multi-agent systems
> Network analysis
> Data-driven predictive control
> Mobility as a Service (MaaS)
MORE DETAILED DESCRIPTION
** Control of mobile multi-agent and networked systems **
Most of Dr. Fabris's research activity has been spent in order to find new strategies, protocols, control techniques and analyze the interactions of multi-agent systems and networks. The following
lists summarizes his main investigations pertaining to the analysis, control and design of mobile multi-agent systems:
- dynamic coverage and dispatch with limited sensing capabilities;
- smart control for robotic formation maintenance highly based on formation control;
- disruption of adversarial semi-autonomous networks;
whereas, the following list illustrates my efforts towards the study stationary networked systems:
- distributed estimation from relative measurements;
- localization and estimation in virtual sensor networks;
- secure and adaptive agreement protocols;
- analysis of the complexity of dynamical networks;
- characterization of certain classes of networks.
** Data-driven predictive control **
This can be intended as a Machine Learning problem in which the goal is to compute the best predictive control without exploiting the classic initial identification step to retrieve the underlying model. Here we look at input/output trajectories from a subspace identification oriented perspective in order to develop a systematic framework to deal with uncertainty in designing data-driven predictive controllers within a stochastic setting. Specifically, by relying on the statistical analysis of the uncertainty in the data-driven predictions, regularization schemes are introduced to limit mismatches between the true outputs and their prediction and, ultimately, improve closed-loop performance.
** Mobility as a service and innovative services **
Smart mobility represents a shift in how we approach transportation, aiming to create efficient, sustainable, and user-centric systems. Digital technologies like IoT, AI, and data analytics drive smart mobility solutions, enabling seamless integration of transportation modes and real-time monitoring. Examples include traffic optimization and monitoring, demand forecasting, public transit enhancement, micromobility management, and predictive maintenance. By leveraging data-driven and OR-based algorithms, my research on MaaS aims to enhance transportation quality, making transport and urban networks more efficient and sustainable.
