Tingzhong Fu
Papers
2
Total Citations
173
H-Index
2
About
Tingzhong Fu is a leading researcher in robotics and neural network control, with a focus on solving critical motion-planning challenges for redundant robot manipulators. His major contributions center on developing innovative varying-parameter neural networks that address the persistent joint-angular-drift problem, which can cause task failures or robot damage in industrial and service applications. His most-cited work, "A Varying-Parameter Convergent-Differential Neural Network for Solving Joint-Angular-Drift Problems of Redundant Robot Manipulators" (2018, 124 citations), introduces the VP-CDNN model, a novel approach that combines quadratic programming with feedback mechanisms to ensure drift-free joint motion. Building on this, his paper "Varying-Parameter RNN Activated by Finite-Time Functions for Solving Joint-Drift Problems of Redundant Robot Manipulators" (2018, 49 citations) proposes the FT-VP-RNN, which achieves finite-time convergence for enhanced safety and efficiency. Fu’s work is notable for its practical impact on real-time robot control, offering robust solutions that improve precision and reliability in automation. With over 170 combined citations, his research is highly influential in advancing neural network-based robotic systems, making him a key figure in the field of intelligent control and robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2