Saeed Rahimi
Papers
3
Total Citations
36
H-Index
3
About
Saeed Rahimi is a robotics researcher whose work focuses on the advanced control and trajectory optimization of parallel robotic manipulators, particularly the 3-DoF Delta robot. His major contributions lie at the intersection of nonlinear control theory, neural network-based system identification, and practical implementation. Rahimi’s most cited work (14 citations) introduces a novel Neural Network self-tuned Inverse Dynamic Controller that leverages an Arc Length Function to achieve smooth trajectory tracking—a critical advancement for high-speed industrial pick-and-place operations. He has also made significant strides in comparing adaptive and sliding mode control strategies (12 citations), using screw theory for rigorous kinematic analysis, and experimentally validating actuator identification via ARMAX and NN-ARX models (10 citations) to enable more accurate motion controller design. Notably, Rahimi’s research bridges the gap between theoretical control algorithms and real-world hardware implementation, providing validated experimental results that are directly applicable to industrial automation. With a growing citation record and a focus on practical, high-performance control of Delta robots, Rahimi is establishing himself as a key contributor to the field of parallel robot control and intelligent actuation systems.
Research Focus
Key Achievements
Top Papers
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