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About

Rafik Euldji is a researcher specializing in advanced control systems for autonomous mobile robotics, with a particular focus on trajectory tracking and path-following problems. His work centers on designing and optimizing hybrid controllers that combine classical and intelligent control techniques—such as fractional order PID (FOPID), backstepping, and adaptive neuro-fuzzy inference systems (ANFIS)—to improve the precision and robustness of wheeled mobile robot navigation. Euldji’s major contributions include the development of an optimal backstepping-FOPID controller, which addresses the challenging parameter tuning problem using hybrid meta-heuristic optimization algorithms. His most cited paper (11 citations) introduces this design for enhanced trajectory tracking, while a subsequent study (5 citations) proposes a combined ANFIS-PID with backstepping technique, comparing performance across multiple meta-heuristic methods. Notably, his 2023 experimental study (2 citations) demonstrates the real-time, low-cost hardware implementation of a hybrid FOPID-backstepping controller on a self-designed mobile robot prototype, bridging the gap between theoretical control design and practical deployment. Euldji’s work is impactful for researchers and engineers seeking efficient, implementable solutions for autonomous robot navigation.