Abdullah Fadhil Mohammed

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About

Abdullah Fadhil Mohammed is a rising researcher in intelligent control systems and industrial robotics, with a focus on optimizing automated manufacturing and mobile robot navigation. His work centers on developing advanced control strategies that combine fractional-order controllers, metaheuristic optimization algorithms, and adaptive neuro-fuzzy inference systems. In his highly cited 2024 paper (28 citations), Mohammed introduced a NARMA-L2 fractional-order ANFIS PD-I controller tuned by the Jaya optimization algorithm for robotic arm-based conveyor belts (RACBs), addressing the four critical motions—joint, motor, gear, and sensor—that underpin modern industrial automation. This work has attracted global attention for its practical improvements in conveyor belt efficiency and precision. Building on this, his 2025 study (9 citations) proposed an accelerated black hole optimization algorithm to enhance a fractional-order PID controller for omni-wheel drive mobile robots, demonstrating his ability to tackle complex, nonlinear system dynamics. Mohammed’s contributions are notable for bridging theoretical optimization with real-world robotic applications, offering scalable solutions for Industry 4.0. His research is particularly valuable for students and engineers seeking robust, computationally efficient control methods for autonomous and industrial systems.