Yingtong Wang
Papers
1
Total Citations
12
H-Index
1
About
Yingtong Wang is a leading researcher in nonlinear control systems and humanoid robotics, with a focus on robust adaptive control strategies for complex, uncertain environments. Their most-cited work introduces a sliding mode nonlinear disturbance observer-based adaptive back-stepping controller (ABSMC), a pioneering algorithm that enhances attitude control for humanoid robotic dual manipulators. This contribution addresses critical challenges in system uncertainty and external disturbances, achieving superior tracking performance and stability—a breakthrough with 12 citations that underscores its influence in the field. Wang’s research bridges theoretical control design and practical robotic applications, advancing the reliability of humanoid robots in dynamic tasks. Their work is notable for integrating disturbance observation with adaptive back-stepping techniques, offering a robust solution for nonlinear uncertain systems. As a researcher, Wang continues to shape the development of intelligent robotic systems, making their contributions essential for students and engineers exploring advanced control theory and humanoid robotics.
Research Focus
Key Achievements
Top Papers
- 1