Moussaab Bounabi
Papers
1
Total Citations
5
H-Index
1
About
Moussaab Bounabi is a researcher specializing in robotics, control systems, and fault detection, with a particular focus on enhancing the reliability and safety of robotic manipulators. His most cited work, "Switched time delay control based on artificial neural network for fault detection and compensation in robot manipulators" (2021), introduces an innovative control scheme that leverages a multilayer perceptron (MLP) artificial neural network to replicate healthy robot behavior, enabling effective detection and compensation of sensor faults. This contribution addresses a critical challenge in robotics—maintaining performance under sensor failures—and has garnered 5 citations, reflecting its relevance in the field of intelligent control. Bounabi’s research bridges artificial intelligence and mechanical systems, offering practical solutions for fault-tolerant robotics. His work is particularly valuable for students and researchers exploring neural network-based control strategies, as it demonstrates how time delay control can be adapted for real-time fault compensation. Bounabi’s achievements highlight his role in advancing robust robotic systems, making his research a key reference for those working on automation, human-robot interaction, and safety-critical applications.
Research Focus
Key Achievements
Top Papers
- 1