M-Emad S. Soliman
Papers
1
Total Citations
4
H-Index
1
About
Dr. M-Emad S. Soliman is a robotics and control systems researcher whose work focuses on intelligent automation and adaptive control strategies for industrial manipulators. His key contributions lie at the intersection of artificial neural networks and fuzzy logic, particularly in developing hybrid control schemes that enhance the precision and adaptability of robotic arms. His most cited work, "Experimental Investigation of an Adaptive Neuro-Fuzzy Control Scheme for Industrial Robots" (2014), presents a novel adaptive fuzzy logic controller with a feed-forward component (AFLCF) for the SCARA robot. In this study, Soliman demonstrates how an artificial neural network, trained off-line, can compute feed-forward torque in real-time, significantly improving trajectory tracking and disturbance rejection. This approach bridges the gap between model-based and learning-based control, offering a practical solution for industrial automation. With 4 citations, this paper has influenced subsequent research in adaptive neuro-fuzzy systems for robotics. Soliman’s work is particularly valuable for students and engineers exploring intelligent control methods that combine the interpretability of fuzzy logic with the learning capabilities of neural networks, making him a notable contributor to the field of robotic control.
Research Focus
Key Achievements
Top Papers
- 1