Zhong-kui Zhang
Papers
1
Total Citations
3
H-Index
1
About
Zhong-kui Zhang is a researcher specializing in robotics, neural network control, and intelligent trajectory planning. His work centers on applying Radial Basis Function (RBF) neural networks to solve complex, nonlinear control problems in robotic systems. Zhang’s most cited paper, “Application of RBF Neural Network in Trajectory Planning of Robot” (2009), demonstrates how RBF networks can achieve arbitrary nonlinear mapping from input to output, enabling robots to accurately follow high-order, nonlinear target trajectories. This contribution addresses a fundamental challenge in robotics: precise motion control under dynamic, real-world conditions. With 3 citations, this foundational work has informed subsequent studies in adaptive control and machine learning for automation. Zhang’s research bridges theoretical neural network algorithms with practical robotic applications, offering a robust framework for improving robot autonomy and precision. His insights are valuable for students and engineers exploring intelligent control systems, nonlinear dynamics, and the integration of artificial intelligence in mechanical systems.
Research Focus
Key Achievements
Top Papers
- 1Application of RBF Neural Network in Trajectory Planning of Robot3 citations · 2009