Lufeng Yin
Papers
1
Total Citations
6
H-Index
1
About
Lufeng Yin is a researcher whose work lies at the intersection of robotics, adaptive control, and neural network-based fault tolerance. His key contributions focus on developing robust, intelligent systems capable of maintaining performance under unexpected failures. In his most cited work, "Robust adaptive fault accommodation for a robot system using a radial basis function neural network" (2001, 6 citations), Yin introduced a discrete-time radial basis function (RBF) neural network designed specifically for fault accommodation in robotic systems. This pioneering approach employs an adaptive dead-zone technique to train network parameters—including weights and centres—ensuring convergence of estimation errors even in the presence of system uncertainties. By enabling robots to autonomously compensate for actuator or sensor faults, Yin's research has laid foundational groundwork for more resilient autonomous systems. Though his citation count reflects a focused, early-career impact, his work remains a reference point for scholars exploring neural adaptive control and fault-tolerant robotics. Yin's methodology demonstrates how biologically inspired learning algorithms can enhance the safety and reliability of complex mechanical systems, making his contributions valuable for students and researchers interested in robust control theory and intelligent automation.
Research Focus
Key Achievements
Top Papers
- 1