Papers
12
Total Citations
369
H-Index
7
About
Xianlun Tang is a multidisciplinary researcher whose work spans robotics control, brain-computer interfaces (BCIs), and intelligent signal processing. His most impactful contribution lies in the intersection of EEG-based motor imagery recognition and advanced neural network architectures — his 2020 paper combining empirical mode decomposition with multi-scale convolutional neural networks has garnered 162 citations, establishing him as a notable voice in BCI research. Tang has also made significant strides in robust control theory for flexible-joint robots (FJRs), developing finite-time disturbance observer frameworks, continuous terminal sliding-mode control strategies, and output feedback schemes that address the persistent challenge of matched and mismatched disturbances in robotic trajectory tracking. His collective work in this domain has accumulated over 140 citations, reflecting strong community adoption. Beyond these core areas, Tang has explored transfer learning for cross-session EEG classification to improve wheelchair control accessibility, mobile robot SLAM optimization using particle swarm methods, and flexible strain sensor design for robotic applications. This breadth positions Tang as a researcher committed to bridging theoretical control systems with real-world intelligent robotic and human-machine interface applications.
Research Focus
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Top Papers
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