activity
20162026
most citedLearning to Boost the Performance of Stable Nonlinear Systems

8 citations · 22 across the 23 of their papers we have counts for

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30 papers · 1 filter

eess.SY2026

Data-Driven Optimal Distributed Controller Synthesis via Spatial Regret

Vaibhav Gupta, Daniele Martinelli, Giancarlo Ferrari-Trecate +2

In this paper, we present a novel method for synthesising an optimal distributed spatial regret controller using experimentally obtained frequency-response data. Spatial regret pro…

eess.SY2026

Stability-Preserving Online Adaptation of Neural Closed-loop Maps

Danilo Saccani, Luca Furieri, Giancarlo Ferrari-Trecate

The growing complexity of modern control tasks calls for controllers that can react online as objectives and disturbances change, while preserving closed-loop stability. Recent app…

eess.SY2026

Safety-Aware Performance Boosting for Constrained Nonlinear Systems

Danilo Saccani, Haoming Shen, Luca Furieri +1

We study a control architecture for nonlinear constrained systems that integrates a performance-boosting (PB) controller with a scheduled Predictive Safety Filter (PSF). The PSF ac…

eess.SY2026

Characterizing all locally exponentially stabilizing controllers as a linear feedback plus learnable nonlinear Youla dynamics

Luca Furieri

We derive a state-space characterization of all dynamic state-feedback controllers that make an equilibrium of a nonlinear input-affine continuous-time system locally exponentially…

eess.SY2025

Distributed Control of Network Systems in the Space of Stabilizing Graph Neural Network Policies

John Cao, Luca Furieri

We study distributed control of networked systems through reinforcement learning, where neural policies must be simultaneously scalable, expressive and stabilizing. We introduce a…

eess.SY2025

A graph-informed regret metric for optimal distributed control

Daniele Martinelli, Andrea Martin, Giancarlo Ferrari-Trecate +1

We consider the optimal control of large-scale systems using distributed controllers whose network topology mirrors the coupling graph between subsystems. In this work, we introduc…