Papers
8
Total Citations
54
H-Index
3
About
M.C.E. Yagoub is a robotics researcher whose work spans intelligent control systems for flexible-joint manipulators and autonomous underwater vehicles (AUVs). His key research areas include neural fuzzy control, fault-tolerant robotics, and underwater robot design. Yagoub’s most cited work, “Hybrid Neural Fuzzy Sliding Mode Control of Flexible-Joint Manipulators with Unknown Dynamics” (2006, 29 citations), introduced a novel control scheme combining feedforward neural networks with fuzzy sliding mode feedback to handle uncertain dynamics in robotic arms. He also made notable contributions to quadrotor safety with a feedback linearization approach for fault tolerance during rotor loss (2018, 7 citations). In underwater robotics, Yagoub advanced AUV efficiency by using intelligent magnetic fields to reduce drag and increase operating depth (2021, 4 citations), and designed fuzzy logic controllers for stable underwater robot balancing (2013, 3 citations). His work on rotating thrusters (2016) and energy-efficient PID control further demonstrates his commitment to practical, robust robotic systems. Yagoub’s research has been implemented on FPGA platforms, showcasing his ability to bridge theoretical control theory with real-world hardware applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2Feedback linearization approach to fault tolerance for a micro quadrotor7 citations · 2018
- 3
- 4
- 5Design and Control of Underwater Robots with Rotating Thrusters3 citations · 2016
- 6
- 7Efficient robot vision system for underwater object tracking3 citations · 2016
- 8