Papers
25
Total Citations
240
H-Index
8
About
Elizaveta Shmalko is a computational control systems researcher whose work sits at the intersection of optimal control theory, symbolic regression, and evolutionary computation. Her research is primarily focused on developing automated methods for synthesizing feedback control systems — a challenging problem in which the goal is to algorithmically derive control functions capable of guiding robots and multi-agent systems to desired states from arbitrary initial conditions. Among her most influential contributions is the formulation of synthesized optimal control under uncertainty, introduced in her 2020 paper (35 citations), which established a rigorous framework for handling bounded additive disturbances while preserving optimality. Complementing this theoretical foundation, her work on Modified Cartesian Genetic Programming (33 citations) and variational analytic programming demonstrated how machine learning and symbolic regression can automate controller design for complex dynamical systems, including flying and mobile robots. Her 2021 paper explicitly bridges control synthesis with machine learning paradigms, advancing the field's conceptual vocabulary. With contributions spanning multi-robot coordination, hybrid evolutionary algorithms, and network operator methods, Shmalko has built a cohesive and growing body of work — accumulating over 170 citations — that meaningfully advances intelligent, automated control for autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Fundamentals of Synthesized Optimal Control35 citations · 2020
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- 5Self-adjusting control for multi robot team by the network operator method14 citations · 2015
- 6Control Synthesis as Machine Learning Control by Symbolic Regression Methods13 citations · 2021
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