Ouamri Bachir

Papers

Total Citations

H-Index

About

Ouamri Bachir is a robotics researcher whose work focuses on the intersection of intelligent control systems and robot programming. His primary research areas include adaptive neuro-fuzzy inference systems, computed torque control, and robot programming by demonstration. Bachir’s most significant contribution lies in addressing the fundamental challenge of model uncertainty in robot manipulator control. His highly cited 2011 paper on adaptive neuro-fuzzy control of the PUMA 600 robot manipulator (37 citations) demonstrates how intelligent systems can overcome the sensitivity of traditional computed torque control to modeling errors and external disturbances. This work has proven foundational for researchers seeking robust control solutions for industrial robots. Bachir further advanced the field by exploring fuzzy logic approaches to computed torque control (2015), continuing to refine methods for handling the complex dynamics of robotic systems. More recently, his 2020 work on trajectory reconstruction for robot programming by example tackles the difficult problem of faithfully reproducing human hand movements with robots, particularly when dealing with complex trajectories containing numerous crossing points. Through these contributions, Bachir has helped bridge the gap between theoretical control methods and practical robotic applications.