Chai Ching Tan
Papers
1
Total Citations
3
H-Index
1
About
Dr. Chai Ching Tan is a researcher whose work bridges advanced control theory and intelligent systems, with a particular focus on nonlinear robotic manipulators. His most-cited paper, "Neural-network-based direct adaptive controller design for nonlinear robotic manipulators" (2002), introduces an innovative all-pass H∞ controller design enhanced by a neural-network-based direct adaptive scheme. This work addresses critical challenges in controlling complex robotic systems, specifically targeting class 1 and class 4 manipulators, and demonstrates how adaptive neural networks can improve stability and performance in nonlinear environments. While his citation count of 3 reflects a niche but specialized audience, his contributions are notable for integrating robust H∞ control with adaptive learning—a forward-looking approach that predates much of today's work in intelligent robotics. Dr. Tan’s research is particularly relevant for students and engineers exploring the intersection of classical control theory and modern machine learning, offering foundational insights into how neural networks can augment traditional controller design for real-world robotic applications.
Research Focus
Key Achievements
Top Papers
- 1