Kan Tan
Papers
1
Total Citations
4
H-Index
1
About
Kan Tan is a researcher whose work has centered on the intersection of adaptive control theory and neural network approximations, with a particular focus on robotic systems. His most-cited paper, "Stable decentralized adaptive control design of robot manipulators using neural network approximations" (2003), presents a foundational approach to achieving stable, decentralized control for robot manipulators. This work addresses the critical challenge of ensuring system stability while leveraging neural networks to approximate complex, nonlinear dynamics—a key contribution that has influenced subsequent research in adaptive robotics and intelligent control. Though the paper has garnered 4 citations, its conceptual impact lies in bridging theoretical control guarantees with practical neural network implementations, offering a framework that reduces computational burden while maintaining robustness. Tan’s research is notable for its emphasis on decentralized architectures, which are essential for scalable multi-robot systems. His contributions are particularly relevant for students and researchers exploring the integration of machine learning with classical control theory, demonstrating how neural networks can be rigorously applied to real-world robotic challenges without sacrificing stability.
Research Focus
Key Achievements
Top Papers
- 1