Donghai Kuang
Papers
1
Total Citations
4
H-Index
1
About
Donghai Kuang is a researcher whose work lies at the intersection of robotics, intelligent control, and neural network applications. His key research areas include admittance control, robot manipulator dynamics, and handling system uncertainties through adaptive learning. His most-cited paper, "Neural-learning enhanced admittance control of a robot manipulator with input saturation" (2017, 4 citations), introduces a sensorless admittance control scheme that integrates radial basis function neural networks (RBFNN) to address system uncertainties and input saturation—a critical challenge in real-world robotic applications. This work demonstrates his ability to combine theoretical rigor with practical constraints, offering a robust solution for compliant robot interaction without the need for force sensors. Kuang’s contributions are particularly valuable for advancing human-robot collaboration and safe automation. While his citation count is still growing, his focus on neural-enhanced control strategies positions him as a promising voice in the field. For students and researchers exploring adaptive control or robot-environment interaction, Kuang’s work provides a clear example of how learning-based methods can overcome traditional control limitations.
Research Focus
Key Achievements
Top Papers
- 1