Ehab Ghith

Ain Shams University

Papers

5

Total Citations

62

H-Index

4

About

Ehab Ghith is a control systems and robotics researcher whose work sits at the intersection of intelligent optimization and precision micro-robotics. His research focuses primarily on the application of meta-heuristic and hybrid optimization algorithms to fine-tune PID controllers for micro-robotic systems — technology with profound implications for minimally invasive medical applications such as targeted drug delivery within the human body. Ghith's most impactful contribution, "Tuning PID Controllers Based on Hybrid Arithmetic Optimization Algorithm and Artificial Gorilla Troop Optimization for Micro-Robotics Systems" (2023), has garnered 40 citations, establishing him as a notable voice in intelligent controller design. His body of work systematically benchmarks and advances optimization strategies — including the Sparrow Search Algorithm, Ant Lion Optimizer, and Rat Swarm Optimization — demonstrating consistent improvement over conventional tuning methods like Ziegler-Nichols. Particularly distinctive is his emphasis on real-time implementation, bridging theoretical optimization with practical engineering deployment. With publications spanning from 2021 to 2025 and a growing citation record totaling over 60 citations, Ghith's research trajectory reflects a disciplined and progressive effort to make micro-robotic systems more precise, reliable, and clinically viable — making his work essential reading for engineers and biomedical researchers alike.

Research Focus

Key Achievements

4
H-Index
5
Papers
62
Total Citations
12
Avg Citations/Paper
🏆 Most Cited Paper
Tuning PID Controllers Based on Hybrid Arithmetic Optimization Algorithm and Artificial Gorilla Troop Optimization for Micro-Robotics Systems
40 citations · 2023
📈 Most Prolific Year: 2022 (2 Papers)
🤝 Key Collaborators: 13
🏛 Institutions: Ain Shams University

Top Papers

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago