Zefeng Hu
Papers
1
Total Citations
4
H-Index
1
About
Zefeng Hu is a researcher specializing in bio-inspired robotics and intelligent control systems, with a particular focus on robotic fish locomotion and adaptive motion planning. His most-cited work, "The Action Control Model for Robotic Fish Using Improved Extreme Learning Machine" (2019), introduces a novel state prediction model that leverages an extreme learning machine optimized by a particle swarm algorithm. This contribution enables robotic fish to achieve fast, accurate adjustments in position and orientation by selecting desirable actions based on precisely predicted states. With 4 citations, this paper demonstrates Hu’s ability to integrate machine learning techniques with real-world robotic applications, addressing critical challenges in underwater vehicle control. His research bridges the gap between theoretical optimization and practical deployment, offering a pathway toward more autonomous and responsive aquatic robots. Hu’s work is particularly notable for its emphasis on real-time adaptability, making it valuable for students and researchers exploring neural network-enhanced control systems in robotics.
Research Focus
Key Achievements
Top Papers
- 1