Papers
2
Total Citations
38
H-Index
2
About
Xingkai Feng is a leading researcher in the field of advanced robotics, with a primary focus on the control systems of omnidirectional mobile robots for industrial inspection applications. His major contributions center on developing robust and adaptive control strategies for nonholonomic systems, particularly for aircraft skin inspection robots that use suction cups for adhesion. Feng’s most cited work, "Robust Adaptive Terminal Sliding Mode Control of an Omnidirectional Mobile Robot for Aircraft Skin Inspection" (2020), has garnered 30 citations and introduced a novel approach to trajectory tracking under challenging conditions. Building on this, his 2022 paper on adaptive neural network tracking control, with 8 citations, further advanced the field by addressing the complex dynamics of these systems through intelligent control methods. Feng’s research is notable for bridging theoretical control engineering with practical robotic applications, directly impacting the safety and efficiency of aircraft maintenance. His work on adaptive neural networks demonstrates a commitment to integrating machine learning with classical control theory, making him a key figure in the development of next-generation autonomous inspection robots.
Research Focus
Key Achievements
Top Papers
- 1
- 2