LI Shao-yuan
Papers
1
Total Citations
3
H-Index
1
About
LI Shao-yuan has made focused contributions at the intersection of neural network theory and robotic control systems. His primary research areas include adaptive control, non-smooth dynamics, and neural network-based robotics, with a particular emphasis on ensuring stability and safety in real-world robotic operations. His most influential work, "RBF neural networks based robot non-smooth adaptive control" (2013), introduced a novel control framework that leverages radial basis function (RBF) neural networks alongside a general error decimal power law to address the challenges of non-smooth adaptive control. This work not only provided rigorous stability analysis—a critical foundation for practical deployment—but also demonstrated its effectiveness through illustrative examples. While his citation count (3) reflects the niche and highly specialized nature of his research, the theoretical rigor and practical relevance of his approach have laid important groundwork for safe robotic operation in uncertain environments. LI’s contributions are particularly valuable for researchers exploring the integration of neural networks with non-smooth control theory, offering a robust mathematical foundation for future advances in adaptive robotics.
Research Focus
Key Achievements
Top Papers
- 1RBF neural networks based robot non-smooth adaptive control3 citations · 2013