41 citations · 146 across the 24 of their papers we have counts for
11 papers · 1 filter
A neural network-based approach to hybrid systems identification for control
Filippo Fabiani, Bartolomeo Stellato, Daniele Masti +1
We consider the problem of designing a machine learning-based model of an unknown dynamical system from a finite number of (state-input)-successor state data points, such that the…
Generalized uncertain Nash games: Reformulation and robust equilibrium seeking -- Extended version
Marta Fochesato, Filippo Fabiani, John Lygeros
We consider generalized Nash equilibrium problems (GNEPs) with linear coupling constraints affected by both local (i.e., agent-wise) and global (i.e., shared resources) disturbance…
Counter-example guided inductive synthesis of control Lyapunov functions for uncertain systems
Daniele Masti, Filippo Fabiani, Giorgio Gnecco +1
We propose a counter-example guided inductive synthesis (CEGIS) scheme for the design of control Lyapunov functions and associated state-feedback controllers for linear systems aff…
Incentives and co-evolution: Steering linear dynamical systems with noncooperative agents
Filippo Fabiani, Andrea Simonetto
Modern socio-technical systems typically consist of many interconnected users and competing service providers, where notions like market equilibrium are tightly connected to the ``…
An active learning method for solving competitive multi-agent decision-making and control problems
Filippo Fabiani, Alberto Bemporad
To identify a stationary action profile for a population of competitive agents, each executing private strategies, we introduce a novel active-learning scheme where a centralized e…
Tracking-based distributed equilibrium seeking for aggregative games
Guido Carnevale, Filippo Fabiani, Filiberto Fele +2
We propose fully-distributed algorithms for Nash equilibrium seeking in aggregative games over networks. We first consider the case where local constraints are present and we desig…
Robust stabilization of polytopic systems via fast and reliable neural network-based approximations
Filippo Fabiani, Paul J. Goulart
We consider the design of fast and reliable neural network (NN)-based approximations of traditional stabilizing controllers for linear systems with polytopic uncertainty, including…
Reliably-stabilizing piecewise-affine neural network controllers
Filippo Fabiani, Paul J. Goulart
A common problem affecting neural network (NN) approximations of model predictive control (MPC) policies is the lack of analytical tools to assess the stability of the closed-loop…
Pursuing robust decisions in uncertain traffic equilibrium problems
Filippo Fabiani
We evaluate the robustness of agents' traffic equilibria in randomized routing games characterized by an uncertain network demand with a possibly unknown probability distribution.…
The optimal transport paradigm enables data compression in data-driven robust control
Filippo Fabiani, Paul J. Goulart
A new data-enabled control technique for uncertain linear time-invariant systems, recently conceived by Coulson et\ al., builds upon the direct optimization of controllers over inp…
On merging constraint and optimal control-Lyapunov functions
Franco Blanchini, Filippo Fabiani, Sergio Grammatico
Merging two Control Lyapunov Functions (CLFs) means creating a single "new-born" CLF by starting from two parents functions. Specifically, given a "father" function, shaped by the…