Amir Seyed Danesh

Information Technology University, University of Malaya

Papers

Total Citations

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About

Amir Seyed Danesh is a leading researcher in intelligent robotic systems, specializing in adaptive control, soft computing, and underactuated robotic manipulation. His work bridges the gap between nonlinear robotic dynamics and machine learning, developing data-driven methods to overcome the limitations of analytical modeling. His most influential paper, "Adaptive control algorithm of flexible robotic gripper by extreme learning machine" (2015), has garnered 59 citations, demonstrating its impact on real-time robotic control. Danesh further advanced the field by applying support vector regression to forecast contact forces in underactuated robotic fingers—a notoriously challenging problem due to strong nonlinearities—earning his 2016 work 33 citations. He also explored neuro-fuzzy estimation for safe joint velocity using conductive silicone rubber sensors, contributing to safer human-robot interaction. Danesh’s contributions are pivotal for students and researchers seeking to integrate machine learning with robotic hardware, offering practical solutions for flexible grippers and sensor-rich manipulators. His work continues to influence adaptive automation and intelligent control systems.