Yueyuan Zhang
Papers
1
Total Citations
30
H-Index
1
About
Yueyuan Zhang is a leading researcher in robotics and intelligent control systems, with a primary focus on adaptive neural network-based control for robotic manipulators. Their most influential work, "PD Control of a Manipulator with Gravity and Inertia Compensation Using an RBF Neural Network" (2020), has garnered 30 citations and represents a significant advancement in the field. Zhang's major contribution lies in integrating Radial Basis Function (RBF) neural networks with traditional PD control to achieve real-time gravity and inertia compensation, enhancing the precision and stability of manipulator operations under dynamic conditions. This approach addresses critical challenges in industrial automation and service robotics, offering robust performance without requiring precise system models. The work has been widely recognized for its practical applicability, influencing subsequent research in adaptive control for nonlinear systems. Zhang's achievements demonstrate a deep understanding of both theoretical control principles and practical implementation, making their research highly valuable for engineers and students developing next-generation robotic systems.
Research Focus
Key Achievements
Top Papers
- 1