Francesco Tedesco
Papers
8
Total Citations
81
H-Index
5
About
Francesco Tedesco is a leading researcher in distributed control systems, with a primary focus on multi-agent coordination, model predictive control (MPC), and resilient autonomy. His work centers on solving formation and motion planning problems for leader–follower multi-agent systems, particularly under constraints and uncertainty. Tedesco’s major contributions include developing set-theoretic receding horizon control schemes that enable safe, constrained operation of autonomous vehicles in cluttered environments—critical for applications like post-disaster first response. His most cited work, “A distributed model predictive control scheme for leader–follower multi-agent systems” (2017, 43 citations), introduces a novel algorithm that has become foundational in the field. He has also advanced resilient control strategies, addressing cyber-physical threats such as replay attacks in networked systems (2020, 4 citations). More recently, Tedesco has tackled dynamic coordination and collision avoidance for multi-mobile robot systems (2025, 2 citations), extending his impact into real-time, safety-critical domains. With a career spanning over a decade, his research bridges theoretical rigor and practical deployment, influencing both academic robotics and industrial automation.
Research Focus
Key Achievements
Top Papers
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- 5A networked-based receding horizon scheme for constrained LPV systems5 citations · 2015
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- 7A networked-based MPC architecture for constrained LPV systems3 citations · 2015
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