Fang Ti
Papers
1
Total Citations
6
H-Index
1
About
Fang Ti is a researcher whose work lies at the intersection of robotics, control theory, and neural network applications. His primary research focus is on developing robust control strategies for robot manipulators operating under uncertainty. Ti’s most notable contribution is his pioneering work on integrating neural network compensators with classical computed torque control. In his highly regarded 2001 paper, "Robust Neural-Network Compensating Control for Robot Manipulator Based on Computed Torque Control," he proposed a novel controller design that uses a Functional Link Neural Network to compensate for system uncertainties, while a robustifying term ensures stability. This approach significantly enhances trajectory tracking accuracy in the presence of unmodeled dynamics and external disturbances. Although his work has accumulated a modest citation count of six, it represents a foundational step in the fusion of adaptive neural networks with traditional robotic control architectures. Ti’s research is particularly valuable for students and engineers seeking to understand how neural networks can be practically applied to improve the performance and reliability of robotic systems in real-world, uncertain environments.
Research Focus
Key Achievements
Top Papers
- 1