Zhe lt Tang
Papers
1
Total Citations
2
H-Index
1
About
Zhe Tang’s research lies at the intersection of intelligent control, fuzzy systems, and humanoid robotics. In his most-cited work, “Dynamic fuzzy neural network for the intelligent control of a humanoid robot” (2011), Tang introduced a novel framework that integrates dynamic fuzzy logic with neural network learning to enable more adaptive and stable control for bipedal locomotion and complex robotic tasks. This contribution addresses a long-standing challenge in robotics: achieving real-time, robust control in uncertain environments without relying on precise mathematical models. While his citation count remains modest, the work demonstrates a forward-looking approach to merging computational intelligence with mechanical systems, laying groundwork for more responsive and human-like robotic behavior. Tang’s research is particularly relevant for students and engineers exploring how soft computing techniques can enhance autonomy in humanoid platforms. His focus on dynamic adaptation and neural-fuzzy synergy marks him as a thoughtful contributor to the evolution of intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1