Hani Hamdan
Papers
4
Total Citations
16
H-Index
2
About
Hani Hamdan is a researcher whose work bridges robotics, control theory, and biomechanics, with a focus on dexterous manipulation and rehabilitation. His key research areas include multifingered robotic hand control, artificial neural networks for grasping optimization, and machine learning-enhanced biomechanical analysis. Hamdan’s major contributions include developing a linearized model for the dynamics of a robot hand-object system, where he applied power system stability concepts—such as participation factors from eigenvectors—to identify instability sources in grasping. This work, published in 1999, remains foundational, with 6 citations. He also advanced optimal control methodology for dexterous robotics hands, using artificial neural networks to optimize grasping and manipulation forces (2010, 6 citations). More recently, Hamdan has explored machine learning for robotic rehabilitation (2025, 2 citations) and biomimetic control architectures for cooperative tasks (2010, 2 citations). His interdisciplinary approach, merging control theory with biomechanics, has influenced both robotic hand design and rehabilitation technologies, making his work notable for students and researchers interested in intelligent, adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
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- 4Biomimetic control architecture for robotic cooperative tasks2 citations · 2010