Elena Sofronova
Russian Academy of Sciences, Peoples' Friendship University of Russia
Papers
18
Total Citations
98
H-Index
6
About
Elena Sofronova is a researcher specializing in optimal control theory, evolutionary computation, and autonomous robotics. Her work sits at the intersection of classical control mathematics and modern computational intelligence, addressing some of the most challenging problems in multi-robot coordination and motion planning. Sofronova's most significant contributions center on developing numerical methods for optimal control problems that are notoriously difficult to solve analytically. Her highly cited 2019 work on genetic programming for mobile robot control (15 citations) and her 2021 comparative study of evolutionary computation approaches (14 citations) demonstrate her commitment to making optimal control practically tractable. She has been particularly innovative in combining the rigorous mathematical foundations of Pontryagin's Maximum Principle with evolutionary algorithms to handle phase constraints and collision avoidance in multi-robot systems — a formidable challenge addressed in her 2020 paper (10 citations). Her earlier research on the network operator method for identification control synthesis (2015–2016, 14 combined citations) showcases her breadth, tackling control design for systems with unknown mathematical models. More recently, she has investigated regularization approaches for non-regular time-optimal problems, pushing theoretical boundaries in state-constrained control. Across her portfolio, Sofronova has established herself as a versatile and rigorous contributor to intelligent robotic control systems.
Research Focus
Key Achievements
Top Papers
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- 10Identification control synthesis by the network operator method4 citations · 2015