Papers
5
Total Citations
63
H-Index
3
About
Federico M. Zegers is a researcher specializing in control systems, multi-agent coordination, and cyber-physical systems, with a focus on handling uncertainty and intermittent communication. His major contributions lie in developing novel controller synthesis and state estimation frameworks for complex, networked systems. Notably, his work on "Distributed State Estimation With Deep Neural Networks for Uncertain Nonlinear Systems Under Event-Triggered Communication" (26 citations) pioneers the use of deep neural networks to approximate system dynamics within sensor networks, enabling robust state estimation despite communication constraints. Zegers has also made significant strides in multi-agent systems, as evidenced by his highly cited paper "Controller Synthesis for Multi-Agent Systems With Intermittent Communication. A Metric Temporal Logic Approach" (24 citations), where he introduced a leader-follower scheme using metric temporal logic to ensure system performance even when communication is sporadic. His research extends to practical applications, such as in "A Hybrid Systems Approach to Dual-Objective Functional Electrical Stimulation Cycling" (3 citations), which addresses rehabilitation robotics by modeling human-robot interaction. With over 60 total citations, Zegers’ work is impactful for students and researchers in control theory, robotics, and AI, offering rigorous yet practical solutions for real-world autonomous systems.
Research Focus
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