Mohamed Djemel

Papers

Total Citations

H-Index

About

Mohamed Djemel’s research lies at the intersection of robotics, control theory, and computational intelligence, with a focused expertise in optimizing the performance of robot manipulators. His major contributions center on the development of hybrid control systems that synergize genetic algorithms with fuzzy logic controllers. In his pivotal 2003 work, Djemel pioneered a two-step methodology: first using genetic algorithms to generate an optimal control sequence for a nonlinear two-link articulated manipulator, then employing this sequence to achieve precise positioning. This framework was further refined in his 2008 study on hybrid genetic-fuzzy controllers, demonstrating how evolutionary computation can systematically tune fuzzy logic parameters for superior trajectory tracking. While his most cited papers have garnered 4 citations each, their foundational nature is evident in their role as early, practical blueprints for merging optimization and soft computing in robotic control. Djemel’s work is particularly notable for addressing the complex, nonlinear dynamics inherent in articulated manipulators, offering a replicable design paradigm that balances computational efficiency with control accuracy. For students and researchers exploring intelligent control, his research provides a clear, methodical entry point into the design of adaptive, self-tuning robotic systems.