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About

Lorenzo Fagiano is a leading researcher in control systems and robotics, with key contributions to multi-agent coordination, model predictive control (MPC), and physics-informed estimation. His work addresses critical challenges in cooperative robotic systems, such as maintaining safety and connectivity under limited communication and time-varying network topologies—essential for applications like search and rescue, environmental monitoring, and hazardous environment operations. Fagiano has pioneered distributed MPC frameworks that enable multi-agent swarms to reconfigure control strategies when agents join or leave the network, while satisfying operational constraints. He also advances gray-box modeling through physics-informed online learning, combining first-principles knowledge with data-driven techniques to improve prediction accuracy in complex systems. His recent work on trajectory planning for tethered robots in uncertain environments tackles the intricate problem of dynamic motion with partially unknown obstacles. With over 25 citations across his most-cited papers from 2023 alone, Fagiano’s research is gaining rapid recognition for its practical impact on autonomous systems. His achievements include developing scalable control solutions that bridge theoretical rigor and real-world deployment, making him a notable figure in modern control engineering.