Anton Glushchenko
Star Center, V. A. Trapeznikov Institute of Control Sciences
Papers
4
Total Citations
32
H-Index
4
About
Anton Glushchenko is a leading researcher in robotics and intelligent control systems, with a primary focus on the stabilization and optimal control of two-wheeled balancing robots. His work addresses fundamental challenges in nonlinear dynamics, feedback linearization, and neural network-based control. Glushchenko’s most cited paper (2018, 11 citations) develops a neural network controller with online training for two-wheeled balancing robots, integrating PID and LQR controllers derived from a state-space mathematical model. He further advanced this field by comparing approximate and partial feedback linearization methods for optimal control (2019, 9 citations), providing critical insights into managing internal dynamics. Beyond mobile robotics, Glushchenko applies neural network models to solve kinematics and control problems for parallel robots (2018, 7 citations), tackling the challenge of indirect output link detection. His recent work (2024, 5 citations) introduces a novel method for monotonous parameter estimation in nonlinearly parameterized regressions, avoiding overparameterization—a significant contribution to adaptive control theory. With a cumulative impact of over 30 citations, Glushchenko’s research bridges theoretical control methods and practical robotic applications, offering valuable frameworks for students and engineers working on autonomous systems and intelligent control.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4