Yassine Lakhal
Abdelmalek Essaâdi University, Université Sultan Moulay Slimane
Papers
4
Total Citations
72
H-Index
4
About
Yassine Lakhal is a robotics researcher whose work focuses on intelligent control and trajectory optimization for multi-articulated robotic manipulators. His key research areas include bio-inspired optimization algorithms, adaptive control systems, and soft computing techniques applied to industrial robotics. Lakhal’s most cited work, “Optimization of Arm Manipulator Trajectory Planning in the Presence of Obstacles by Ant Colony Algorithm” (2017, 37 citations), demonstrates how Ant Colony Optimization (ACO) can efficiently solve complex path-planning problems, a critical challenge in automated manufacturing. He further advanced this line of research by developing an ACO-tuned PID controller (2023, 16 citations), replacing traditional trial-and-error tuning with a robust, nature-inspired optimization method for precise position control. Lakhal has also contributed to intelligent control strategies, proposing a Multi-input Multi-output Fuzzy Logic Controller (2015, 12 citations) for complex, two-link manipulators, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller (2014, 7 citations) that combines neural networks with fuzzy logic for dynamic system modeling. His work bridges the gap between theoretical optimization algorithms and practical robotic applications, offering scalable solutions for industrial automation. With a growing citation footprint, Lakhal’s research continues to influence the development of more efficient, adaptive, and autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
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