S. Yaqubi
Papers
1
Total Citations
2
H-Index
1
About
S. Yaqubi is a researcher whose work lies at the intersection of robotics, control systems, and deep learning, with a particular focus on flexible link manipulators (FLMs). Their key contributions center on addressing the complex challenges of modeling, deflection correction, and end-point control for heavy-duty robotic arms. In their most-cited work, "Deep Learning-Based Deflection Correction and End-Point Control of Heavy-Duty Vertical Single-Link Flexible Manipulators" (2024, 2 citations), Yaqubi proposed a novel approach that integrates deep learning techniques to correct structural deflections in real time, enabling precise control of flexible manipulators. This work is significant because it tackles a critical bottleneck in the development of lightweight, energy-efficient robots and humanoid systems, where traditional rigid-body control methods fall short. By advancing the modeling and control of single-link flexible manipulators (SLFMs), Yaqubi’s research contributes directly to safer, more adaptable robotic systems capable of operating in dynamic environments. Though early in its citation impact, this work signals a promising trajectory in the field of intelligent robotic control, with potential applications spanning industrial automation, assistive robotics, and beyond.
Research Focus
Key Achievements
Top Papers
- 1