Papers
4
Total Citations
12
H-Index
2
About
Yaqiang Liu is a researcher at the forefront of soft robotics and intelligent control systems, with a secondary focus on advanced medical imaging technologies. His primary contributions lie in the modeling and trajectory tracking control of biomimetic robotic systems, particularly rope-driven soft robotic arms (RDSRA). Inspired by the bending and adsorption structures of an elephant trunk and the lamellar structure of a gecko toe, Liu’s work enables these compliant arms to grasp objects of diverse shapes and materials. To address the inherent challenges of uncertain robotic systems, he has pioneered novel control frameworks, including a neural network-based augmented high-order control barrier function (NN-AHoCBF) for ensuring safety under input-output constraints, and dynamic event-triggered adaptive fuzzy admittance control for managing system uncertainties. These contributions, published in 2025, have already garnered early citations, reflecting their immediate relevance. Earlier in his career, Liu also contributed to the calibration of monolithic PET detectors, using gamma interaction position distribution constraints to enhance imaging accuracy. His work bridges theoretical control advances with practical robotic applications, establishing him as an emerging voice in adaptive and safety-critical robotics.
Research Focus
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