Mauro Franceschelli

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Institutional webpage: https://unica.it/unica/page/en/mauro_franceschelli

Short biography

Mauro Franceschelli is currently an Associate Professor (Professore di II Fascia) in Control and Systems Theory and Engineering (Automatica) at the Department of Electrical and Electronic Engineering, University of Cagliari since July 2022. He received the B.S. and M.S. degree in Electronic Engineering “cum laude” in 2005 and 2007 from the University of Cagliari, Italy. He received the PhD from the same university in 2011 where he worked as  postdoc until Septemebr 2015. He has spent visiting periods at the Royal Institute of Technology (KTH), the Georgia Institute of Technology (GaTech), the University of California at Santa Barbara (UCSB), Xidian University, Xi’an, China and CNRS, Grenoble, France. From October 2015 to June 2019 he has been Assistant Professor (RTD-A) at the Department of Electrical and Electronic Engineering, University of Cagliari, Italy, with a position founded by the Italian Ministry of Education, University and Research (MIUR) under the 2014 call  “Scientific Independence of Young Researchers” (SIR). From July 2019 to June 2022 he has been tenure-track Assistant Professor (RTD-B)

He has published more than 90 papers in top international journals and conference proceedings and directed or participated to more than 13 european/national/regional research projects.

He was Associate Editor for the IEEE Transactions on Automation Science and Engineering (T-ASE) (2021-2023) and  is now Associate Editor for IFAC Nonlinear Analysis: Hybrid Systems (NAHS) since 2023, and Guest Associate Editor (2024) for the IEEE Open Journal of Control Systems.   

He is member of the Conference Editorial Board (CEB) for the IEEE Control Systems Society (CSS) since 2019. He serves as Associate Editor for the IEEE Conference on Automation Science and Engineering (CASE) since 2015, the IEEE American Control Conference (ACC) since 2019 and IEEE Conference on Decision and Control since 2020.

He is IEEE Senior Member and Officer (Secretary) for the IEEE Italy Section Chapter of the IEEE Control Systems Society. 

His area of expertise is the development of distributed control and coordination algorithms for large groups of autonomous systems that interact within a communication or sensing network and suffer from limited information availability and constrained resources. His research interests include consensus problems, gossip algorithms, multi-agent systems, multi-robot systems, nonsmooth analysis, distributed optimization and electric demand side management.

He received the italian sciantific habilitation for access to a position of full professor (professore di I fascia), disciplinary sector ING-INF/04, Automatica, in 06/02/2023, valid from 06/02/2023 to 06/02/2033 (art. 16, comma 1, Legge 240/10).

[Curriculum Vitae]

Short summary of research interests

My research interests are control, coordination and estimation algorithms for networks of embedded systems. This interest consists in both novel methodologies to analyse the emergent behaviour of systems with simple and properly defined local interactions and several applications such as the coordination problem for networks of unmanned aerial vehicles or mobile robots, sensor networks or sets of processing units. The so called “emerging behaviour” in a networked system can be defined as the property of a set of locally interacting systems to achieve a global objective. Examples are the optimization of an objective function which depends upon all the systems’ states such as energy consumption, task execution/delivery time, lifetime of a set of systems, robustness against single system failure or achievement of the so called “consensus” state, i.e., a particular network state in which all state variables have a common value. The problem of how to reach such consensus state is currently being thoroughly investigated from several different perspectives depending on the particular application under consideration. In particular, the use of consensus algorithms based on the so called “gossip” communication scheme to solve load balancing and task assignment problems has been particularly successful. A gossip algorithm makes use of asynchronous and random local state updates (from simple averaging to complex interactions) between the nodes that compose the network to achieve a goal such as that of globally minimizing the maximum task execution time. Therefore, these distributed algorithms constitute an example of networked system that by exploiting only local interactions achieves a meaningful emergent behaviour, i.e., the optimization of an objective function that models a quantity of interest.

My area of expertise consists in the development of decentralized control and coordination algorithms for autonomous systems that interact within a communication or sensing network and suffer from limited information availability and constrained resources. My approach consists in the development of interaction mechanisms between systems so that the global “emerging” behaviour of the interconnected systems achieves some desired goal from an engineering perspective.

The most difficult challenge ahead is the development of the next generation of control and coordination protocols for the multitude of interacting systems that will revolutionize our society in the near future: starting from interacting smart appliances and mobile robots that will perform the most various tasks in our homes to the control of large scale and complex systems such as smart urban environments or the smart grid where new control, coordination and estimation problems pop up with increasing frequency due to the ever increasing abundance of communication bandwidth and processing power of modern technology.

My research keywords are:

• Multi-Agent Systems;

• Multi-robot systems;

• Distributed electric demand side management and response;

• Agents and Autonomous Systems;

• Coordination and Control of Networked Systems;

• Consensus Algorithms;

• Planning and Scheduling;

• Distributed optimization;

• Resilient control and optimization;

• Learning for Control.