59 citations · 68 across the 6 of their papers we have counts for
7 papers
Transfer Meta-Learning for State Space Autoencoders
Stefano De Carli, Daniele Masti, Davide Previtali +1
Nonlinear system identification often requires costly experimental campaigns and separate model fitting for each different instance (target system) of a given system family. To red…
Fault-tolerant control of nonlinear systems: An inductive synthesis approach
Daniele Masti, Davide Grande, Andrea Peruffo +1
Actuator faults heavily affect the performance and stability of control systems, an issue that is even more critical for systems required to operate autonomously under adverse envi…
Certified Inductive Synthesis for Online Mixed-Integer Optimization
Marco Zamponi, Emilio Incerto, Daniele Masti +1
In fields such as autonomous and safety-critical systems, online optimization plays a crucial role in control and decision-making processes, often requiring the integration of cont…
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…
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…
Railway cyber-security in the era of interconnected systems: a survey
Simone Soderi, Daniele Masti, Yuriy Zacchia Lun
Technological advances in the telecommunications industry have brought significant advantages in the management and performance of communication networks. The railway industry is a…
A machine-learning approach to synthesize virtual sensors for parameter-varying systems
Daniele Masti, Daniele Bernardini, Alberto Bemporad
This paper introduces a novel model-free approach to synthesize virtual sensors for the estimation of dynamical quantities that are unmeasurable at runtime but are available for de…