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About

Fuwen Yang is a leading researcher in the intersection of control theory, robotics, and intelligent systems, with key contributions to visual servoing, multi-agent systems, and sensor technology. His work on learning-based control of robotic visual servoing systems—a survey that has garnered 68 citations—provides a comprehensive foundation for integrating machine learning with robotic vision. Yang pioneered robust localization methods for mobile robots using extended H∞ filtering (30 citations), addressing nonlinear kinematic challenges in autonomous navigation. He has also advanced fault-tolerant and intrusion-resistant group control for multi-agent systems under cyber attacks, with applications to robotic manipulators (22 citations). Notably, Yang developed an ultra-low-cost graphite-on-paper pressure sensor for robot grippers (21 citations), demonstrating exceptional response time and trivial fabrication. His recent digital twin framework for robust control of robotic-biological systems (17 citations) bridges computational modeling with medical device regulation, while his hybrid deep-Q-network and model predictive control approaches (16 citations) push the boundaries of point stabilization in visual servoing. With over 185 total citations across his most-cited works, Yang’s research has profound implications for autonomous robotics, cyber-physical security, and affordable sensor design, making him a pivotal figure in modern control and robotics engineering.