Zan Xiao
Papers
1
Total Citations
3
H-Index
1
About
Zan Xiao’s research centers on intelligent control systems for robotic manipulators, with a particular focus on adaptive and self-organizing neural network architectures. Their most-cited work introduces a self-structured organizing single-input control system based on a differentiable cerebellar model articulation controller (CMAC) for n-link robot manipulators. This innovative approach achieves high-precision position tracking by dynamically adjusting its structure during operation, eliminating the need for pre-defined network configurations. The single-input CMAC controller simplifies implementation while maintaining robust performance, offering a practical solution for complex tasks such as de-icing robot manipulation. Although their citation count remains modest, Xiao’s contributions demonstrate a clear commitment to advancing adaptive control theory for real-world robotic applications. Their work provides a foundation for future developments in self-tuning neural controllers, particularly in environments requiring precise, adaptive motion control.
Research Focus
Key Achievements
Top Papers
- 1