Modeling, analysis and control of large-scale systems

Complex networks are dynamical systems where many interacting agents produce emergent collective behaviours. I build descriptive models and design validated control strategies to orchestrate collective dynamics.

Multi-agent Systems Control across scales Collective behaviour

Modeling, analysis and control of large-scale systems

Large ensembles of interacting agents often give rise to nontrivial macroscopic behaviours. Capturing essential dynamics while keeping models tractable is challenging but necessary to design controllers that scale. My work focuses on parametrization of networked models, stability and performance analysis, and distributed control techniques to shape collective behaviours reliably.

Related Publications

G.C. Maffettone, D. Salzano, M. di Bernardo

Robust Macroscopic Density Control of Heterogeneus Multi-Agent Systems — submitted to IEEE Transactions on Automatic Control, 2026.

L. Catello, I. Napolitano, D. Salzano, M. di Bernardo

Sparse shepherding control of large-scale multi-agent systems via Reinforcement Learning — submitted to the European Control Conference, 2026.

Giusti, A., Salzano, D., di Bernardo, M., & Gorochowski, T. E.

Data-driven inference of digital twins for high-throughput phenotyping of motile and light-responsive microorganisms. Journal of the Royal Society Interface 22, 20250780 (2026).

M. Coraggio, D. Salzano, M. di Bernardo

“Controlling complex systems”. Encyclopedia of Systems and Control Engineering, Springer (2025).

Della Rossa, F., Salzano, D., Di Meglio, A., De Lellis, F., Coraggio, M., Calabrese, C., ... & Di Bernardo, M.

A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic. Nature Communications, 11(1), 5106 (2020).