Enes Rahic
Papers
2
Total Citations
47
H-Index
2
About
Enes Rahic has made significant contributions to the field of robotics and control systems, with a particular focus on iterative learning control (ILC) for flexible robot arms. His research centers on enhancing the precision and performance of robotic systems that exhibit joint flexibility—a common challenge in real-world automation. Rahic’s major contribution lies in the innovative use of accelerometers to estimate arm angles, enabling more accurate control of flexible manipulators. His most cited work, “On the use of accelerometers in iterative learning control of a flexible robot arm” (2007, 26 citations), demonstrates how integrating accelerometer data with motor angle measurements improves ILC algorithms. Similarly, his earlier paper “Iterative learning control of a flexible robot arm using accelerometers” (2005, 21 citations) laid the groundwork for this approach, showcasing experimental validation on a laboratory-scale arm. These studies have influenced subsequent research in adaptive and learning-based control for flexible structures, with combined citations exceeding 47. Rahic’s work is notable for bridging theoretical control design with practical implementation, offering a robust solution to vibration and accuracy issues in robotics. His findings remain relevant for students and researchers exploring advanced control strategies in mechatronics and automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2Iterative learning control of a flexible robot arm using accelerometers21 citations · 2005