Papers
15
Total Citations
110
H-Index
7
About
Selman Djeffal is an emerging robotics researcher whose work sits at the intersection of continuum robotics, computational intelligence, and control systems. His research focuses primarily on solving the notoriously challenging kinematic and dynamic modeling problems inherent to continuum robots — highly flexible, biologically inspired systems with enormous potential in surgical and industrial applications. Djeffal has made significant contributions by applying artificial intelligence and optimization techniques to continuum robot kinematics. His work employing artificial neural networks for inverse kinematic modeling and comparing meta-heuristic algorithms — including Particle Swarm Optimization, TLBO, and others — has provided researchers with practical, computationally efficient alternatives to intractable analytical solutions. More recently, he has pioneered the application of deep reinforcement learning, specifically DDPG, for controlling multi-section spatial continuum robots, representing a forward-looking step toward autonomous robotic control. His contributions to dynamic modeling and optimized control strategies, particularly for cable-driven surgical robots, demonstrate a commitment to real-world clinical relevance. With a growing citation record exceeding 90 citations across his published works, and notable firsts such as introducing Optimized Computer Torque Control to continuum robotics, Djeffal is establishing himself as a productive voice in this specialized, rapidly evolving field.
Research Focus
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Top Papers
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