Advanced control based on Recurrent Neural Networks learned using Virtual Reference Feedback Tuning and application to an Electronic Throttle Body (with supplementary material). - William D'Amico 001; Marcello Farina; Giulio Panzani (CoRR, 2021)

Data-based control design for linear discrete-time systems with robust stability guarantees. - William D'Amico 001; Marcello Farina (CDC, 2022)

Recurrent Neural Network controllers learned using Virtual Reference Feedback Tuning with application to an Electronic Throttle Body. - William D'Amico 001; Marcello Farina; Giulio Panzani (ECC, 2022)

An incremental input-to-state stability condition for a generic class of recurrent neural networks. - William D'Amico 001; Alessio La Bella; Marcello Farina (CoRR, 2022)

Data-Based Control Design for Output-Error Linear Discrete-Time Systems With Probabilistic Stability Guarantees. - William D'Amico 001; Andrea Bisoffi; Marcello Farina (IEEE Control. Syst. Lett., 2023)

Virtual reference feedback tuning for linear discrete-time systems with robust stability guarantees based on set membership. - William D'Amico 001; Marcello Farina (Autom., 2023)

LMI-Based Control Design with Robust Local Stability Guarantees for Linear Discrete-Time Systems with Input Saturations. - William D'Amico 001; Simone Zanini; Alessio La Bella; Marcello Farina (ECC, 2024)

An Incremental Input-to-State Stability Condition for a Class of Recurrent Neural Networks. - William D'Amico 001; Alessio La Bella; Marcello Farina (IEEE Trans. Autom. Control., 2024)

Regional stability conditions for recurrent neural network-based control systems. - Alessio La Bella; Marcello Farina; William D'Amico 001; Luca Zaccarian (Autom., 2025)

Data-driven control of echo state-based recurrent neural networks with robust stability guarantees. - William D'Amico 001; Alessio La Bella; Marcello Farina (Syst. Control. Lett., 2025)