Hongliang Yin
Beihang University, Harbin Institute of Technology, Peking University
Papers
4
Total Citations
14
H-Index
3
About
Hongliang Yin is a robotics and control systems researcher whose work centers on intelligent control strategies for robotic systems operating under conditions of uncertainty. His research primarily explores the intersection of neural network methodologies and classical control theory, with a particular focus on adaptive compensation, trajectory tracking, and variable structure control for both terrestrial robot manipulators and the more complex domain of free-floating space robotics. Yin's most notable contributions involve developing hybrid control frameworks that combine radial-basis-function (RBF) neural networks with sliding-mode variable structure control, grounded in Lyapunov stability theory. These approaches allow robotic systems to adaptively learn and compensate for unknown nonlinearities and external disturbances without requiring precise a priori system models — a significant practical advantage in real-world deployment. His 2011 work on neural network adaptive compensation for free-floating space robots is particularly distinctive, addressing the uniquely challenging dynamics that arise when a robot operates in microgravity environments where base movement couples with manipulator motion. With publications spanning 2010 to 2011 and accumulating citations across multiple works, Yin's research offers foundational contributions to intelligent, uncertainty-robust robot control, making his work relevant reading for students pursuing adaptive control, space robotics, or neural-network-based engineering solutions.
Research Focus
Key Achievements
Top Papers
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