Yuhan Xiong
Papers
9
Total Citations
150
H-Index
5
About
Yuhan Xiong is a dynamic researcher working at the intersection of robotics, control theory, and brain-computer interface (BCI) systems. Their work spans two compelling domains: intelligent human-machine interaction and safety-critical control for robotic manipulators. Xiong's early notable contribution introduced the MVMD-CCA algorithm for SSVEP-based BCI systems, cleverly combining multivariate variational mode decomposition with canonical correlation analysis to enhance neural signal recognition — a paper that has garnered 62 citations and demonstrated practical application in robot control. This work highlights their versatility in bridging neuroscience-inspired interfaces with robotic systems. The majority of Xiong's research, however, focuses on advancing safety-critical control frameworks for robotic manipulators. Their contributions include addressing real-world challenges such as uncertain dynamic models, unmeasured joint velocities, input delays, and input saturation — problems that significantly complicate safe robot operation. Through innovative use of control barrier functions (CBFs), high-order CBFs, and extended state observers, Xiong has developed rigorous yet practical safety guarantees, accumulating over 35 additional citations across multiple publications. Their progressive body of work, culminating in prescribed-time safety control and whole-body safety methods, positions Yuhan Xiong as an emerging authority in robust, safety-guaranteed robotics — research increasingly vital as autonomous systems enter real-world environments.
Research Focus
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