Shao He
Papers
1
Total Citations
2
H-Index
1
About
Shao He is a researcher in robotics and control systems, with a primary focus on adaptive control strategies for space robotics. Their most notable contribution is the development of an adaptive PID control method based on radial basis function (RBF) neural networks for uncertain space robot systems, specifically targeting free-floating dual-arm configurations. This work, published in 2011, addresses the critical challenge of precise joint and carrier control in the presence of system uncertainties—a key issue for autonomous space operations. While the paper has garnered 2 citations, it represents foundational work in applying neural network-based adaptive control to the complex dynamics of space robots, offering a comparative advantage over traditional control methods. Shao He’s research sits at the intersection of neural networks, adaptive control, and space robotics, contributing to the advancement of autonomous systems for extraterrestrial manipulation and assembly tasks. Their work is particularly relevant for students and researchers exploring robust control solutions for uncertain, nonlinear systems in aerospace applications.
Research Focus
Key Achievements
Top Papers
- 1Adaptive control based on neural network for uncertain space robot2 citations · 2011