Shahaboddin Shamshirband
Papers
8
Total Citations
224
H-Index
6
About
Shahaboddin Shamshirband has carved a distinctive niche at the intersection of soft computing, robotics, and intelligent control systems. His research focuses on developing adaptive algorithms to address the profound nonlinearities inherent in robotic manipulators, particularly underactuated and compliant grippers. Shamshirband’s major contributions lie in applying machine learning and fuzzy logic to solve complex modeling and control challenges. Notably, his work on adaptive control using extreme learning machines (cited 59 times) and fuzzy adaptive differential evolution (cited 57 times) has provided robust frameworks for system identification and control of robot manipulators. He pioneered the use of support vector regression to forecast contact forces in underactuated robotic fingers, a method that circumvents the difficulty of analytical modeling. His research also extends to neuro-fuzzy methodologies for predicting joint strain and safe velocities in passive robotic fingers, integrating embedded sensors for real-time feedback. Despite several retractions, his core body of work—spanning adaptive control, soft computing, and robotic gripper design—has garnered significant attention, with his most impactful papers accumulating over 150 citations. Shamshirband’s contributions are particularly valuable for researchers exploring intelligent, data-driven approaches to robotic manipulation.
Research Focus
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Top Papers
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