Velimir Todorovski
Papers
1
Total Citations
7
H-Index
1
About
Velimir Todorovski is an emerging researcher specializing in safe control systems, learning-based methods, and formal safety guarantees for dynamical systems. His work sits at the intersection of control theory and machine learning, with a particular focus on addressing one of the field's most pressing challenges: ensuring safety constraints are satisfied in systems whose dynamics are not fully known. His most notable contribution, "Learning-Based Prescribed-Time Safety for Control of Unknown Systems With Control Barrier Functions" (2024), introduces a Gaussian process-based framework that extends Control Barrier Function (CBF) methodology to unknown dynamical systems, guaranteeing state constraint satisfaction within a user-defined prescribed time horizon. This work meaningfully advances the field by bridging the gap between theoretically rigorous safety guarantees — previously limited to systems with well-characterized dynamics — and the practical reality of uncertain or partially observed systems. With 7 citations already accrued shortly after publication, the work has attracted early attention from the control and robotics communities. Todorovski's research holds significant promise for safety-critical applications such as autonomous vehicles, robotics, and aerospace systems, where constraint violations can have severe consequences.
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Top Papers
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