Alexander Nazin
Papers
2
Total Citations
27
H-Index
2
About
Alexander Nazin is a leading figure in robust control theory and optimization for uncertain dynamical systems, with a particular focus on Lagrangian systems and electromechanical actuators. His most influential work, "Integral Sliding Mode Convex Optimization in Uncertain Lagrangian Systems Driven by PMDC Motors: Averaged Subgradient Approach" (2020, 21 citations), pioneers a novel integration of sliding mode control with convex optimization to handle the complexities of permanent magnet DC motors under uncertainty. This approach, using an averaged subgradient method, enables precise state regulation despite incomplete system knowledge. Nazin’s subsequent research, "Robust Tracking as Constrained Optimization by Uncertain Dynamic Plant: Mirror Descent Method and ASG—Version of Integral Sliding Mode Control" (2023, 6 citations), extends these ideas to robust tracking problems, employing mirror descent algorithms to achieve constrained optimization in real-time control. His work bridges theoretical rigor with practical applicability, offering engineers new tools for controlling uncertain systems with measurable states. Nazin’s contributions are particularly notable for advancing the synergy between optimization theory and sliding mode techniques, making him a key innovator in modern robust control.
Research Focus
Key Achievements
Top Papers
- 1
- 2