Hala Mostafa

Princess Nourah bint Abdulrahman University, RTX (United States)

Papers

Total Citations

H-Index

About

Hala Mostafa is a leading researcher in robotics and control systems, with a primary focus on enhancing the precision and robustness of robotic manipulators. Her most cited work, "Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation" (2022, 60 citations), tackles the critical challenge of trajectory tracking in nonlinear systems plagued by parametric and model uncertainties. By integrating fast finite-time sliding mode control with neural network-based friction compensation, Mostafa has developed a novel framework that achieves rapid convergence and exceptional robustness, directly addressing the brittleness of complex electromechanical systems. Her earlier, pioneering work, "A Morphogenetically Assisted Design Variation Tool" (2013), draws inspiration from natural morphogenesis to capture expert knowledge as functional blueprints, aiming to make tightly integrated systems more adaptable to changing requirements. This cross-disciplinary approach—merging control theory, neural networks, and bio-inspired design—underscores her unique contribution to making robots more reliable and easier to modify. With a growing citation impact and a clear trajectory toward solving real-world robotic challenges, Mostafa’s research is essential reading for engineers and students seeking to advance adaptive, high-performance automation.