S. Elnaggar
Papers
5
Total Citations
97
H-Index
4
About
S. Elnaggar is a prominent robotics and control systems researcher whose work centers on advanced motion control, kinematic analysis, and intelligent optimization techniques for robotic manipulators. With a growing citation record exceeding 97 citations, Elnaggar has made significant contributions to the field of trajectory tracking and robust controller design. Elnaggar's most impactful work, garnering 44 citations, introduced the enhanced Artificial Bee Colony algorithm with multi-elite guidance (MGABC) to optimally tune PID controllers for 3-DOF robotic manipulators, demonstrating superior performance over conventional optimization approaches. Complementing this, their research on optimized sliding mode controllers for flexible-joint manipulators addresses critical real-world challenges including noise rejection, disturbance cancellation, and system nonlinearity, with one study employing gray wolf optimization for enhanced robustness. A recurring theme across Elnaggar's portfolio is the rigorous evaluation of kinematic analysis tools, exemplified by a highly cited 2024 case study on KUKA industrial robots and earlier foundational work on Stewart platform kinematics. Together, these contributions position Elnaggar as a versatile researcher bridging theoretical control design with practical industrial robotics, offering valuable insights for engineers and students working on next-generation automation systems.
Research Focus
Key Achievements
Top Papers
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