Raihane Mechgoug
Papers
2
Total Citations
14
H-Index
2
About
Raihane Mechgoug is a researcher specializing in intelligent control systems for autonomous robotics, with a focus on neural networks and fuzzy logic. Her work addresses critical challenges in trajectory tracking and path following for unmanned aerial and ground vehicles. In her highly cited 2023 paper, she introduced a novel adaptive neural network-based compensation control strategy for quadrotors, combining radial basis function neural networks (RBFNN) with integrator backstepping to achieve robust trajectory tracking—a contribution that has already garnered 8 citations. Earlier, her 2013 study on path following for autonomous mobile robots demonstrated the power of fusing fuzzy logic with neural networks to enhance autonomy and decision-making, earning 6 citations. Mechgoug’s research bridges theory and practice, offering scalable solutions for real-world robotic navigation in uncertain environments. Her work is particularly notable for its emphasis on adaptive, learning-based approaches that reduce reliance on precise mathematical models, making her a key contributor to the growing field of intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
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