Learning-driven Nonlinear Optimal Control via Gaussian Process Regression. - Lorenzo Sforni; Ivano Notarnicola; Giuseppe Notarstefano (CDC, 2021)

GoPRONTO: a Feedback-based Framework for Nonlinear Optimal Control. - Lorenzo Sforni; Sara Spedicato; Ivano Notarnicola; Giuseppe Notarstefano (CoRR, 2021)

Structured-policy Q-learning: an LMI-based Design Strategy for Distributed Reinforcement Learning. - Lorenzo Sforni; Andrea Camisa; Giuseppe Notarstefano (CDC, 2022)

On-Policy Data-Driven Linear Quadratic Regulator via Combined Policy Iteration and Recursive Least Squares. - Lorenzo Sforni; Guido Carnevale; Ivano Notarnicola; Giuseppe Notarstefano (CDC, 2023)

A Distributed Online Optimization Strategy for Cooperative Robotic Surveillance. - Lorenzo Pichierri; Guido Carnevale; Lorenzo Sforni; Andrea Testa; Giuseppe Notarstefano (ICRA, 2023)

Receding Horizon CBF-Based Multi-Layer Controllers for Safe Trajectory Generation. - Lorenzo Sforni; Giuseppe Notarstefano; Aaron D. Ames (ACC, 2024)

Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient. - Lorenzo Sforni; Guido Carnevale; Ivano Notarnicola; Giuseppe Notarstefano (CoRR, 2024)

Multi-Robot Target Monitoring and Encirclement via Triggered Distributed Feedback Optimization. - Lorenzo Pichierri; Guido Carnevale; Lorenzo Sforni; Giuseppe Notarstefano (CoRR, 2024)

DATA-DRIVEN PRONTO: a Model-free Solution for Numerical Optimal Control. - Marco Borghesi; Lorenzo Sforni; Giuseppe Notarstefano (CoRR, 2025)

A Distributed Feedback-Based Framework for Nonlinear Aggregative Optimal Control. - Lorenzo Sforni; Guido Carnevale; Giuseppe Notarstefano (IEEE Trans. Autom. Control., 2025)