Hasan Esen
Papers
1
Total Citations
16
H-Index
1
About
Hasan Esen is a researcher whose work sits at the intersection of advanced control theory and machine learning, with a primary focus on enhancing the robustness and efficiency of model predictive control (MPC). His key contributions lie in developing event-triggered MPC frameworks that intelligently reduce control update frequencies while maintaining system stability, even under significant model uncertainties. His most cited work (2017, 16 citations) tackles a critical limitation of event-triggered MPC—the tendency for excessive triggering in the presence of large disturbances—by integrating machine learning to compensate for model inaccuracies. This innovative hybrid approach demonstrates his ability to bridge theoretical control design with practical, data-driven solutions. While his citation count reflects a growing body of work, the technical depth and forward-looking nature of his research position him as a contributor to the next generation of adaptive, resource-aware control systems. His work is particularly relevant for applications in autonomous systems, robotics, and industrial process control, where computational efficiency and disturbance rejection are paramount.
Research Focus
Key Achievements
Top Papers
- 1