collaborators

6 papers

eess.SY2026

Observing the state of networks with directed higher-order interactions

Roberto Rizzello, Davide Salzano, Stefano Boccaletti +1

We consider the problem of reconstructing the state of a network of nonlinear dynamical systems in the presence of directed higher-order interactions. Grounded on analytical conver…

eess.SY2026

Sparse shepherding control of large-scale multi-agent systems via Reinforcement Learning

Luigi Catello, Italo Napolitano, Davide Salzano +1

We propose a Reinforcement Learning framework for sparse indirect control of large-scale multi-agent systems, where few controlled agents shape the collective behavior of many unco…

eess.SY2026

Robust multi-scale leader-follower control of large multi-agent systems

Davide Salzano, Gian Carlo Maffettone, Mario di Bernardo

In many multi-agent systems of practical interest, such as traffic networks or crowd evacuation, control actions cannot be exerted on all agents. Instead, controllable leaders must…

eess.SY2026

Robust Macroscopic Density Control of Heterogeneous Multi-Agent Systems

Gian Carlo Maffettone, Davide Salzano, Mario di Bernardo

Modern applications, such as orchestrating the collective behavior of robotic swarms or traffic flows, require the coordination of large groups of agents evolving in unstructured e…

eess.SY2025

A bioreactor-based architecture for in vivo model-based and sim-to-real learning control of microbial consortium composition

Sara Maria Brancato, Davide Salzano, Davide Fiore +3

Microbial consortia offer significant biotechnological advantages over monocultures for bioproduction. However, industrial deployment is hampered by the lack of scalable architectu…

eess.SY2025

Controlling Complex Systems

Marco Coraggio, Davide Salzano, Mario di Bernardo

This chapter provides a comprehensive overview of controlling collective behavior in complex systems comprising large ensembles of interacting dynamical agents. Building upon tradi…