William D'Amico

12 papers Journal 9Unranked 3
YearRankTypeTitle / Venue / Authors
2025 J jnl
Data-driven control of echo state-based recurrent neural networks with robust stability guarantees.
Syst. Control. Lett.
William D'Amico, Alessio La Bella, Marcello Farina
2025 J jnl
Regional stability conditions for recurrent neural network-based control systems.
Autom.
Alessio La Bella, Marcello Farina, William D'Amico, Luca Zaccarian
2024 J jnl
An Incremental Input-to-State Stability Condition for a Class of Recurrent Neural Networks.
IEEE Trans. Autom. Control.
William D'Amico, Alessio La Bella, Marcello Farina
2024 — conf
LMI-Based Control Design with Robust Local Stability Guarantees for Linear Discrete-Time Systems with Input Saturations.
ECC
William D'Amico, Simone Zanini, Alessio La Bella, Marcello Farina
2024 J jnl
Regional stability conditions for recurrent neural network-based control systems.
CoRR
Alessio La Bella, Marcello Farina, William D'Amico, Luca Zaccarian
2023 J jnl
Data-Based Control Design for Output-Error Linear Discrete-Time Systems With Probabilistic Stability Guarantees.
IEEE Control. Syst. Lett.
William D'Amico, Andrea Bisoffi, Marcello Farina
2023 J jnl
Virtual reference feedback tuning for linear discrete-time systems with robust stability guarantees based on set membership.
Autom.
William D'Amico, Marcello Farina
2022 J jnl
An incremental input-to-state stability condition for a generic class of recurrent neural networks.
CoRR
William D'Amico, Alessio La Bella, Marcello Farina
2022 — conf
Data-based control design for linear discrete-time systems with robust stability guarantees.
CDC
William D'Amico, Marcello Farina
2022 — conf
Recurrent Neural Network controllers learned using Virtual Reference Feedback Tuning with application to an Electronic Throttle Body.
ECC
William D'Amico, Marcello Farina, Giulio Panzani
2022 J jnl
Virtual Reference Feedback Tuning for linear discrete-time systems with robust stability guarantees based on Set Membership.
CoRR
William D'Amico, Marcello Farina
2021 J jnl
Advanced control based on Recurrent Neural Networks learned using Virtual Reference Feedback Tuning and application to an Electronic Throttle Body (with supplementary material).
CoRR
William D'Amico, Marcello Farina, Giulio Panzani