5 papers
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…
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…
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…
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…
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…