Papers
8
Total Citations
114
H-Index
5
About
Heyu Hu is a robotics researcher whose work centers on advanced control systems for robotic manipulation, with particular expertise in dual-arm robot coordination, impedance control, and adaptive control strategies. His research addresses one of the field's most persistent challenges: enabling robots to interact reliably and precisely with uncertain, real-world environments where model inaccuracies and dynamic disturbances are unavoidable. Hu's most recognized contribution, "Adaptive Variable Impedance Control of Dual-Arm Robots for Slabstone Installation" (2021, 38 citations), demonstrates his strength in bridging theoretical control design with demanding practical applications. His work on impedance sliding mode control with adaptive fuzzy compensation (2020, 24 citations) further established his reputation for developing robust position-force control solutions that accommodate model uncertainty. Notably, his research on prescribed-time tracking control without velocity measurement (2023, 24 citations) highlights his innovative approach to reducing sensor dependency while maintaining rigorous performance guarantees. Across his portfolio, Hu consistently integrates neural network-based learning, optimization, and constraint-handling techniques — as seen in his neuro-adaptive cooperative control framework (2024) — pushing dual-arm robotic systems toward greater autonomy and reliability. His growing citation record reflects a meaningful and expanding influence on intelligent robot control research.
Research Focus
Key Achievements
Top Papers
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- 5Practical Tracking Control for Dual-arm Robot with Output Constraints8 citations · 2022
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