Ahmed Zidan
Papers
4
Total Citations
12
H-Index
3
About
Ahmed Zidan is a researcher specializing in robotics and control systems, with a focused expertise in the auto-tuning of controllers for robotic manipulators. His work centers on applying advanced optimization algorithms—particularly Particle Swarm Optimization (PSO) and Multi-Objective Particle Swarm Optimization (MOPSO)—to automatically determine optimal gain values for PD and PID controllers, ensuring precise trajectory tracking. Zidan’s major contributions include developing practical, implementation-ready methods that replace manual tuning with automated, data-driven approaches, significantly enhancing the efficiency and accuracy of robotic motion control. His comparative studies, such as evaluating MOPSO against Multi-Objective Cuckoo Search (MOCS), provide critical insights into the relative performance of metaheuristic algorithms in control applications. While his most-cited papers each hold 3 citations, they represent foundational work in a niche area, demonstrating consistent impact within the robotics and optimization communities. Zidan’s research bridges theoretical optimization and practical robotics, offering valuable tools for engineers seeking robust, autonomous tuning solutions. His achievements underscore a commitment to advancing intelligent control systems, making his work a reference point for students and researchers exploring automated controller design.
Research Focus
Key Achievements
Top Papers
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