Yuehua Huang

China Three Gorges University, Southwest University

Papers

Total Citations

130

H-Index

About

Yuehua Huang is a leading researcher in neural network optimization and intelligent robotic systems. Their most influential work centers on developing finite-time Zhang neural networks for solving time-varying quadratic programs, a breakthrough that has enabled real-time adaptive control in robotic tracking. This foundational paper, with 128 citations, has become a key reference in the field of dynamic optimization and neural computation. Huang's contributions bridge theoretical advances in neural dynamics with practical applications, particularly in robotics and automation. More recently, they have ventured into soft robotics and tactile sensing, exemplified by their 2025 work on a super-hydrophobic tactile sensor for damage-free fruit grasping. Though newly published, this innovation highlights Huang's commitment to solving real-world challenges in agricultural robotics and human-robot interaction. Their work demonstrates a rare ability to move from abstract mathematical frameworks to tangible engineering solutions, making them a versatile and impactful figure in computational intelligence and robotics.