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About

A. S. Koposov is a control theorist whose research advances the frontier of iterative learning control (ILC) for multi-agent systems operating under uncertainty. Their work addresses a critical challenge in modern smart manufacturing: how to coordinate networks of robots performing high-precision, repetitive tasks when those tasks and communication topologies can change between iterations. Koposov’s key contributions include developing networked modifications of ILC laws that minimize deviation in the presence of random perturbations—external disturbances and measurement noise—as detailed in their most-cited work (2020, 19 citations). They further extended this framework to handle variable reference trajectories (2022, 7 citations) and stochastic multi-agent systems with dynamic network topologies (2023, 2 citations). By tackling the intersection of ILC, multi-agent coordination, and stochastic robustness, Koposov is helping to enable the next generation of flexible, resilient, and high-precision robotic manufacturing systems. Their research is particularly valuable for students and engineers working on networked robotics, where the ability to adapt to changing tasks and noisy environments is essential for real-world deployment.