Coordination of a Research Project: OPT4SMART (Distributed Optimization Methods for Smart Cyber-Physical Networks).
Brumali, Riccardo; Carnevale, Guido; Notarstefano, Giuseppe, Data-Driven Distributed Optimization via Aggregative Tracking and Deep-Learning, «IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS», 2026, Early access, pp. 1 - 12 [Scientific article]
Borghesi, M.; Bosso, A.; Notarstefano, G., MR-ARL: Model Reference Adaptive Reinforcement Learning for Robustly Stable On-Policy Data-Driven LQR, «IEEE TRANSACTIONS ON AUTOMATIC CONTROL», 2026, 71, pp. 1129 - 1144 [Scientific article]
Sforni, Lorenzo; Carnevale, Guido; Notarstefano, Giuseppe, A Distributed Feedback-based Framework for Nonlinear Aggregative Optimal Control, «IEEE TRANSACTIONS ON AUTOMATIC CONTROL», 2025, 70, pp. 3784 - 3799 [Scientific article]Open Access
Messilem, A. Mohamed; Brumali, Riccardo; Carnevale, Guido; Notarstefano, Giuseppe; Carli, Ruggero, A Distributed MILP-ADMM Framework for Italian Energy Communities: Shared Energy Incentives, Renewables, and Shiftable Loads, in: IFAC-PapersOnLine, Elsevier B.V., 2025, 59, pp. 19 - 24 (atti di: 1st IFAC Workshop on Smart Energy System for Efficient and Sustainable Smart Grids and Smart Cities, SENSYS 2025, Politecnico di Bari, ita, 2025) [Contribution to conference proceedings]Open Access
Brumali, Riccardo; Carnevale, Guido; Notarstefano, Giuseppe, A Feedback-Based Distributed Method for Multiscale Optimal Control of Multi-Agent Systems, in: 2025 IEEE 64th Conference on Decision and Control (CDC), 2025, pp. 1863 - 1868 (atti di: 2025 IEEE 64th Conference on Decision and Control (CDC), Rio de Janeiro, Brazil, 09-12/12/2025) [Contribution to conference proceedings]
Testa, Andrea; Carnevale, Guido; Notarstefano, Giuseppe, A Tutorial on Distributed Optimization for Cooperative Robotics: From Setups and Algorithms to Toolboxes and Research Directions, «PROCEEDINGS OF THE IEEE», 2025, 113, Article number: 10969623 , pp. 40 - 65 [Scientific article]Open Access
Carnevale, Guido; Mimmo, Nicola; Notarstefano, Giuseppe, A Unifying System Theory Framework for Distributed Optimization and Games, «IEEE TRANSACTIONS ON AUTOMATIC CONTROL», 2025, Early Access, pp. 1 - 16 [Scientific article]
Carnevale, Guido; Bastianello, Nicola; Notarstefano, Giuseppe; Carli, Ruggero, ADMM-Tracking Gradient for Distributed Optimization Over Asynchronous and Unreliable Networks, «IEEE TRANSACTIONS ON AUTOMATIC CONTROL», 2025, 70, Article number: 10876579 , pp. 5160 - 5175 [Scientific article]Open Access
Carnevale, Guido; Notarstefano, Giuseppe, Almost Sure Convergence in Feedback Optimization via Stochastic Timescale Separation, in: 2025 IEEE 64th Conference on Decision and Control (CDC), 2025, pp. 585 - 590 (atti di: 2025 IEEE 64th Conference on Decision and Control (CDC), Rio de Janeiro, Brazil, 09-12/12/2025) [Contribution to conference proceedings]
Carnevale, Guido; Notarstefano, Giuseppe, Data-Driven nonlinear optimal control via feedback-based extremum seeking, in: 2025 IEEE 64th Conference on Decision and Control (CDC), 2025, pp. 1024 - 1029 (atti di: 2025 IEEE 64th Conference on Decision and Control (CDC), Rio de Janeiro, Brazil, 09-12/12/2025) [Contribution to conference proceedings]
Bosso, A.; Borghesi, M.; Iannelli, A.; Notarstefano, G.; Teel, A. R., Derivative-free data-driven control of continuous-time linear time-invariant systems, «EUROPEAN JOURNAL OF CONTROL», 2025, 86, Article number: 101309 , pp. 1 - 8 [Scientific article]
Brumali, Riccardo; Carnevale, Guido; Notarstefano, Giuseppe, Distributed learning and optimization of a multi-agent macroscopic probabilistic model, «EUROPEAN JOURNAL OF CONTROL», 2025, 86, Article number: 101332 , pp. 1 - 7 [Scientific article]
Carnevale, Guido; Bastianello, Nicola; Notarstefano, Giuseppe; Carli, Ruggero, Distributed Newton Optimization with ADMM-Based Consensus, in: Berberich, Jiulian Iannelli, Andrea Allgower, Frank, Systems Theory in Data and Optimization. SysDO 2024. Lecture Notes in Control and Information Sciences - Proceedings, Berlino, Springer, 2025, pp. 313 - 329 (atti di: Systems Theory in Data and Optimization. SysDO 2024., Stuttgart, Germany, September 30, 2024 – October 2, 2024) [Contribution to conference proceedings]
Bosso, Alessandro; Borghesi, Marco; Serrani, Andrea; Notarstefano, Giuseppe; Teel, Andrew R., On-Policy Data-Driven Linear Quadratic Optimal Control of SISO Systems via Model Reference Adaptive Reinforcement Learning, in: Proceedings of the IEEE Conference on Decision and Control, 2025, pp. 2642 - 2647 (atti di: 2025 IEEE 64th Conference on Decision and Control (CDC), Rio de Janeiro, Brazil, 09-12 December 2025) [Contribution to conference proceedings]
