Fengqin Wang
Papers
2
Total Citations
15
H-Index
2
About
Fengqin Wang is a researcher specializing in robotics and control systems, with a focus on constrained robotic manipulators and inspection robotics. Her most impactful work, "Neural network–based terminal sliding mode applied to position/force adaptive control for constrained robotic manipulators" (2018, 12 citations), introduces a novel control strategy that integrates terminal sliding mode control with neural networks to address dynamic uncertainties in constrained robotic systems. This approach uniquely combines position and velocity tracking to enhance force/position control, offering significant advancements in adaptive robotics. Wang also contributed to the field of power transmission line inspection with her 2007 study on mechanical structure design for inspection robots, which proposed an innovative robot architecture and predicted future trends in the domain. While her citation counts reflect a focused research impact, her work demonstrates a commitment to solving practical challenges in robotic control and structural design, particularly in applications requiring precision and adaptability. Wang’s research bridges theoretical control methods with real-world robotic applications, making her contributions valuable for students and engineers exploring adaptive control, neural networks, and inspection robotics.
Research Focus
Key Achievements
Top Papers
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- 2