Shijun Tang
Papers
1
Total Citations
14
H-Index
1
About
Shijun Tang is a leading researcher in robotics and neural network control, with a focus on solving complex, time-varying kinematic problems for mobile manipulators. His major contribution is the development of a novel noise suppression zeroing neural network (NSZNN) for online trajectory tracking. In his most-cited work, Tang addresses the time-varying inverse kinematics (TVIK) problem for a four Mecanum wheeled mobile manipulator (FMWMM) under external disturbances—a critical challenge in real-world automation. By creating a holistic kinematic model and integrating noise suppression into the zeroing neural network framework, his approach achieves robust, real-time performance where traditional methods fail. This work has garnered 14 citations since its 2024 publication, reflecting its immediate impact on the field. Tang’s research bridges theoretical neural dynamics and practical robotic control, offering a powerful tool for autonomous systems in noisy, dynamic environments. His ongoing work promises to advance the reliability and precision of mobile manipulators in industrial and service applications.
Research Focus
Key Achievements
Top Papers
- 1