Penghui Fan
Papers
1
Total Citations
6
H-Index
1
About
Penghui Fan is a rising researcher in the field of intelligent robotics and nonlinear control systems, with a focus on adaptive neural control and event-triggered mechanisms. Their most-cited work, "Event-triggered adaptive neural prescribed performance admittance control for constrained robotic systems without velocity measurements" (2024), addresses a critical challenge in robotics: achieving precise and safe interaction in constrained environments without relying on velocity sensors. This paper introduces a novel framework that combines prescribed performance bounds with neural network approximation, ensuring both stability and transient response under limited sensing. Although early in their career, Fan’s work has already garnered 6 citations, signaling growing recognition for its practical relevance in human-robot collaboration and rehabilitation robotics. By eliminating the need for velocity measurements, their approach reduces hardware costs and enhances system reliability, making it particularly valuable for real-world applications. Fan’s contributions are paving the way for more adaptive, sensor-efficient robotic systems, and their research trajectory suggests a promising future in advancing intelligent control theory.
Research Focus
Key Achievements
Top Papers
- 1