Papers
9
Total Citations
102
H-Index
4
About
Dan Huang is a robotics and control systems researcher whose work spans two complementary domains: legged robot locomotion and advanced control algorithms for robotic manipulators. His most impactful contribution, "Dynamic Neural Networks Based Kinematic Control for Redundant Manipulators with Model Uncertainties" (2018, 62 citations), demonstrates his expertise in applying intelligent control methods to address real-world uncertainties in robotic arms — a challenge central to modern automation and manufacturing. Alongside this, Huang has made substantial contributions to quadruped robot research, developing gait planning strategies for crawling, trotting, and stair traversal on irregular terrain, with particular emphasis on high-payload and rescue applications. His use of Double-Support Triangle theory, Zero Moment Point trajectory tracking, and Posture Feedback Compensation reflects a rigorous, mathematically grounded approach to locomotion stability. More recently, his work on load-sensitive impedance control for electrohydrostatic actuators signals a growing interest in hydraulic and physically robust robotic systems. With research spanning over a decade and citations across robotics, control theory, and autonomous systems, Huang represents a versatile contributor whose work bridges theoretical control design and practical robot deployment in challenging environments.
Research Focus
Key Achievements
Top Papers
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- 3Quadruped robot crawl gait planning based on DST10 citations · 2014
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- 6Modeling and Optimal Control of Rescue Quadruped Robot with High Payload4 citations · 2016
- 7Quadruped robot gait planning with stair environment constraints3 citations · 2014
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