Mikhail Emelianov

Nizhny Novgorod State Technical University

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About

Mikhail Emelianov is a researcher focused on the theory and application of iterative learning control (ILC) and two-dimensional (2D) systems, particularly for improving the precision of repetitive robotic operations. His work addresses the fundamental challenge of enhancing system accuracy by leveraging data from previous task repetitions to refine control signals. Emelianov’s major contributions include the development of higher-order ILC algorithms for linear systems, which extend classical methods to achieve faster convergence and better performance. His 2024 paper on this topic has already garnered 3 citations, signaling growing interest in his approach. Additionally, his 2016 study on the weak stability of nonlinear repetitive processes—a class of 2D systems with roots in physical process modeling—advanced the theoretical understanding of stability beyond the established exponential property. With 2 citations, this work provides a foundation for analyzing more complex, real-world systems. Emelianov’s research bridges control theory and practical robotics, offering tools for industries reliant on high-precision repetitive tasks. His ongoing exploration of higher-order algorithms and nonlinear dynamics positions him as a rising contributor to the field of learning-based control.