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About

Julia Emelianova is a leading researcher in the field of iterative learning control (ILC), with a particular focus on multi-agent systems and networked robotics. Her work addresses critical challenges in modern smart manufacturing, where robots must execute high-precision repetitive operations while connected via networks and subject to random perturbations. Emelianova's major contributions include developing ILC algorithms that maintain performance under changing reference trajectories, random disturbances, and measurement noises—problems that are central to the reliability of autonomous robotic systems. Her most cited paper, "Iterative Learning Control of a Multiagent System under Random Perturbations" (2020, 19 citations), introduces networked modifications of ILC laws that minimize deviation in the presence of external disturbances. She has also advanced the field by designing higher-order ILC algorithms for linear systems and addressing actuator nonlinearities, a practical issue that often limits real-world implementation. Her work on weak stability of nonlinear repetitive processes (2016) provides foundational theory for these applications. With a growing citation record and recent publications extending into 2025, Emelianova is establishing herself as a key voice in the intersection of control theory, robotics, and networked systems.