Papers

8

Total Citations

67

H-Index

5

About

Hameed Ullah is a control systems and robotics researcher whose work sits at the intersection of nonlinear control theory, observer design, and autonomous robotic systems. His research predominantly addresses the challenging problem of output feedback control for flexible-joint robotic manipulators — systems notorious for their nonlinear dynamics, parametric uncertainties, and susceptibility to external disturbances. His most influential contribution, garnering 30 citations, introduced a robust high-gain observer-based control framework for single-link flexible-joint robot manipulators that elegantly reduces the number of required sensing elements, offering significant practical advantages for real-world implementation. Ullah has made repeated contributions to high-gain and extended high-gain observer methodologies, demonstrating their effectiveness in rejecting noise and disturbances across multiple robotic platforms. Beyond ground-based manipulators, his research extends into aerial robotics, including vision-based autonomous UAV tracking and, more recently, hybrid force/position control for aerial manipulators capable of sustained horizontal force delivery — a frontier problem in physical human-robot interaction. His work on sampled-data control and model predictive control further reflects the breadth of his expertise. With a cumulative citation record spanning multiple high-impact problems in modern robotics, Ullah represents a productive voice advancing practical, mathematically rigorous control solutions for next-generation robotic systems.

Research Focus

Key Achievements

5
H-Index
8
Papers
67
Total Citations
8
Avg Citations/Paper
🏆 Most Cited Paper
Robust Output Feedback Control of Single-Link Flexible-Joint Robot Manipulator with Matched Disturbances Using High Gain Observer
30 citations · 2021
📈 Most Prolific Year: 2020 (4 Papers)
🤝 Key Collaborators: 16
🏛 Institutions: National University of Sciences and Technology, University of Naples Federico II

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago