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Danilo Saccani

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SCI-STI-GFT

ME-717: Neural Networks for Optimal Control

Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology

Danilo Saccani, Melanie Nicole Zeilinger

In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from ...

IEEE2023

Optimal distributed control with stability guarantees by training a network of neural closed-loop maps

Giancarlo Ferrari Trecate, Luca Furieri, Danilo Saccani, Leonardo Massai

This paper proposes a novel approach to improve the performance of distributed nonlinear control systems while preserving stability by leveraging Deep Neural Networks (DNNs). We build upon the Neural System Level Synthesis (Neur-SLS) framework and introduc ...

Institute of Electrical and Electronics Engineers Inc.2024

Unconstrained Learning of Networked Nonlinear Systems via Free Parametrization of Stable Interconnected Operators

Giancarlo Ferrari Trecate, Luca Furieri, Danilo Saccani, Leonardo Massai

This paper characterizes a new parametrization of nonlinear networked incrementally L2 -bounded operators in discrete time. The distinctive novelty is that our parametrization is free - that is, a sparse large-scale operator with bounded incremental L2 gai ...

Institute of Electrical and Electronics Engineers Inc.2024