Abdelhalim Boutarfa

About

Abdelhalim Boutarfa is a researcher focused on enhancing the reliability and autonomy of robotic systems through advanced fault diagnosis and fault-tolerant control. His work centers on applying computational intelligence—particularly artificial neural networks and fuzzy logic—to detect and isolate sensor and actuator failures in industrial manipulators. In his most cited work (2014, 9 citations), Boutarfa pioneered a scheme for fault detection and isolation (FDI) in three-link SCARA robots, demonstrating how soft computing techniques can extend traditional diagnostic methods. He further advanced the field by integrating sliding mode observers for fault-tolerant control (2013, 6 citations), enabling robots to continue performing tasks without immediate human intervention after internal failures. His research addresses a critical need in modern manufacturing: ensuring robotic systems can autonomously cope with malfunctions. With a cumulative impact of over 18 citations across his key papers, Boutarfa’s contributions provide a foundation for more resilient and self-repairing industrial robots, making his work essential reading for students and engineers interested in intelligent fault management and the practical application of soft computing in robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Fault diagnosis in robotic manipulators using artificial neural networks and fuzzy logic

   9 citations · 2014
2. 2
   Fault tolerant control on robotic manipulator using sliding mode observers

   6 citations · 2013
3. 3
   Sensor and Actuator Fault Diagnosis Based on Soft Computing Techniques

   3 citations · 2014

Key Collaborators

Contact & Links