Papers
22
Total Citations
370
H-Index
10
About
Xiaoqi Tang is a robotics and control systems researcher whose work spans industrial robot calibration, advanced control methodologies, and emerging sensing technologies. Tang's most influential contributions lie in developing sophisticated calibration frameworks for industrial robots, with landmark papers on joint-dependent geometric error identification, elasto-geometrical calibration, and kinematic parameter estimation using laser displacement sensors garnering over 35–56 citations each. These works directly address the precision demands of modern manufacturing, offering practical solutions to the longstanding challenges of positional accuracy and error measurement in serial robotic systems. Beyond calibration, Tang has made meaningful contributions to robot control theory, including a Fourier series-based learning controller for nonlinear manipulators (42 citations) and iterative data-driven fractional model reference control for precise speed tracking. Tang's cascade path-tracking and constrained iterative feedback tuning approaches further demonstrate a consistent commitment to robust, real-world control solutions. More recently, Tang has expanded into flexible electronics, contributing to encapsulated stretchable amphibious strain sensors suited for health monitoring, wearable systems, and underwater robotics. With a body of work spanning over two decades and consistently cited across robotics and control engineering communities, Tang represents a versatile and impactful figure bridging precision engineering, intelligent control, and next-generation sensing.
Research Focus
Key Achievements
Top Papers
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- 2A learning controller for robot manipulators using Fourier series42 citations · 2000
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- 6Encapsulated stretchable amphibious strain sensors27 citations · 2024
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