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About

Mostafa Mjahed is a researcher whose work spans intelligent control systems, robotics, and computational intelligence, with particular focus on fuzzy logic-based control, mobile robot navigation, and nonlinear system optimization. His most impactful contributions lie in the development of advanced adaptive interval type-2 fuzzy sliding mode controllers, most notably demonstrated in his 2018 studies addressing robust tracking control for n-link robot manipulators and MIMO nonlinear systems with unknown dynamics and disturbances — works that have collectively garnered over 35 citations. By combining interval type-2 fuzzy logic systems with sliding mode control, Mjahed has pushed the boundaries of handling parametric uncertainties in complex robotic systems. Beyond control theory, his research extends to autonomous mobile robot navigation, where he has designed intelligent behavior fusion architectures implemented on FPGA hardware and explored hybrid metaheuristic optimization strategies. His 2021 hybrid PSO-GWO algorithm and 2023 improved elephant herding optimization approach reflect a sustained commitment to advancing path planning under uncertain, real-world conditions. Through this breadth of work — bridging theoretical control design with practical robotics applications — Mjahed has established himself as a productive contributor to intelligent systems engineering, offering students and practitioners valuable frameworks for tackling dynamic, uncertain robotic environments.