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20182024
most citedReliably-stabilizing piecewise-affine neural network controllers

41 citations · 146 across the 24 of their papers we have counts for

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6 papers · 1 filter

eess.SY2021★ 41 cited

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…

math.OC2021

Learning equilibria with personalized incentives in a class of nonmonotone games

Filippo Fabiani, Andrea Simonetto, Paul J. Goulart

We consider quadratic, nonmonotone generalized Nash equilibrium problems with symmetric interactions among the agents. Albeit this class of games is known to admit a potential func…

stat.ME2021

Asymptotic Incidence Rate estimation of SARS-COVID-19 via a Polya process scheme: a comparative analysis in Italy and European Countries

Filippo Carone Fabiani

During an ongoing epidemic, especially in the case of a new agent, data are partial and sparse, also affected by external factors as for example climatic effects or preparedness an…

eess.SY2021

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.…

math.OC2021★ 3 cited

Extremum seeking control of a class of constrained nonlinear systems

Shuai Yuan, Filippo Fabiani, Simone Baldi

This paper studies the extremum seeking control (ESC) problem for a class of constrained nonlinear systems. Specifically, we focus on a family of constraints allowing to reformulat…

math.OC2021

Probabilistic stabilizability certificates for a class of black-box linear systems

Filippo Fabiani, Kostas Margellos, Paul J. Goulart

We provide out-of-sample certificates on the controlled invariance property of a given set with respect to a class of black-box linear systems. Specifically, we consider linear tim…