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About

Evgeniy Krastev is a robotics and control systems researcher whose work sits at the intersection of iterative learning control, robotic manipulator dynamics, and motion planning. His most significant contribution lies in advancing Iterative Learning Control (ILC) for systems operating under real-world constraints — a challenging problem largely overlooked in prior literature. His 2017 paper on State Space Constrained ILC for robotic manipulators, garnering 31 citations, established a rigorous framework for accurate trajectory tracking that fully accounts for the dynamic constraints inherent in industrial robotic operations. Building on this foundation, Krastev developed Constrained Output ILC for nonlinear systems and explored bounded error algorithms, collectively pushing the boundaries of high-precision repetitive control. His research extends into redundant robot arm motion planning, where he applies sliding mode control and mathematical modeling to replicate the flexibility of human hand movement. More recently, he introduced a graph-oriented approach to robot arm dynamics modeling, offering a novel interdisciplinary perspective on a classical problem. His work on biped robot design with anthropomorphic gait further demonstrates his breadth across humanoid robotics. With over 60 cumulative citations, Krastev's contributions meaningfully advance constrained control theory and its practical application in modern robotics.