A.M. Danilova

Peoples' Friendship University of Russia

Papers

Total Citations

H-Index

About

A.M. Danilova is a researcher specializing in optimal control theory and symbolic regression methods. Her work addresses a fundamental challenge in control systems: bridging the gap between numerical and analytical solution approaches. In her most-cited paper, "Solution of the optimal control problem by symbolic regression method" (2021, 3 citations), Danilova introduces a novel framework that applies symbolic regression to derive analytical solutions for optimal control problems. This approach overcomes the limitations of traditional direct and indirect methods—where indirect methods can yield analytical forms but impose restrictive conditions on problem dimensionality and structure. By leveraging symbolic regression, Danilova enables the discovery of closed-form control laws without requiring prior knowledge of the solution structure, expanding the class of problems solvable analytically. Her contribution is particularly valuable for complex, nonlinear systems where conventional techniques fail. While her citation count is modest, the work represents a promising intersection of machine learning and control theory, with potential applications in robotics, aerospace, and autonomous systems. Danilova’s research continues to explore how data-driven symbolic methods can transform optimal control design.