Basel Jouda
Papers
1
Total Citations
3
H-Index
1
About
Basel Jouda is a researcher focused on advancing intelligent control systems for robotics, with a particular emphasis on reinforcement learning and adaptive mechanisms to handle real-world uncertainties. His key research areas include model-free control, friction compensation, and the application of machine learning to robotic manipulation. Jouda’s most notable contribution is the development of an online reinforcement learning controller designed to mitigate the detrimental effects of high-variation friction forces in robotic arm joints—a challenge that often leads to reduced accuracy and costly operational disruptions. By proposing a model-free approach, his work offers a practical solution for maintaining performance without requiring precise system models, addressing a critical gap in industrial robotics. Although his highly cited paper, published in 2023, has garnered 3 citations to date, its relevance to improving robotic arm reliability under dynamic conditions marks it as a foundational step toward more resilient automation. Jouda’s research holds promise for enhancing efficiency in manufacturing and other settings where friction variability poses persistent challenges, establishing him as an emerging voice in adaptive robotic control.
Research Focus
Key Achievements
Top Papers
- 1