Abdelhamid Ghoul
Papers
11
Total Citations
92
H-Index
6
About
Abdelhamid Ghoul is an emerging robotics researcher whose work centers on the modeling, control, and optimization of continuum robots — flexible, biologically inspired manipulators with significant promise in surgical and industrial applications. His research addresses some of the field's most persistent challenges: solving complex kinematic models, designing robust controllers, and bridging the gap between theoretical frameworks and practical implementation. Ghoul has made notable contributions through the application of artificial neural networks to solve inverse kinematic models of both spatial and planar variable curvature continuum robots, work that has accumulated over 20 citations across related publications. He has pioneered the application of advanced control strategies to continuum systems, including nonlinear sliding mode control (18 citations), reinforcement learning via DDPG (14 citations), and optimized computed torque control — the latter applied for the first time to continuum robots. His dynamic modeling work for cable-driven systems directly addresses challenges in surgical robotics, earning 10 citations since 2024. Beyond continuum robotics, Ghoul has contributed to classical manipulator control, developing a fuzzy computed torque controller for the PUMA 560. His hands-on approach extends to physical prototyping, including 3D-printed cable-driven robots validated using neural network-based inverse kinematics, demonstrating a productive integration of simulation, optimization, and experimental verification.
Research Focus
Key Achievements
Top Papers
- 1Optimized Nonlinear Sliding Mode Control of a Continuum Robot Manipulator18 citations · 2022
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