Papers
37
Total Citations
490
H-Index
13
About
Deqing Huang is a prominent control systems researcher whose work spans iterative learning control (ILC), robotic systems, and human-robot interaction. His research has made substantial contributions to advancing intelligent control frameworks for complex, uncertain dynamic systems, with particular emphasis on discrete-time nonlinear systems and flexible robotic manipulators. Huang's most influential work focuses on ILC theory and applications. His 2015 paper on ILC design for linear discrete-time systems with multiple high-order internal models has garnered 91 citations, establishing foundational methodologies widely adopted in the field. He has also pioneered spatial iterative learning control for robotic path learning, enabling robots to autonomously adapt to unknown environments through interaction-based feedback. His contributions extend to vibration suppression in flexible manipulators, multi-agent synchronization, and robust adaptive control under parametric and nonparametric uncertainties. More recently, Huang has directed his expertise toward human-robot collaboration, developing learning-based controllers that prescribe interaction forces and proactively learn human movement intentions—work with direct implications for rehabilitation robotics and assistive exoskeletons. His robust repetitive learning approaches for hydraulic exoskeleton systems further demonstrate the real-world applicability of his theoretical innovations. Collectively, his publications reflect a researcher bridging rigorous control theory with meaningful engineering impact.
Research Focus
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Top Papers
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- 3Spatial iterative learning control for robotic path learning35 citations · 2022
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