Jiantao Yang
Papers
1
Total Citations
8
H-Index
1
About
Dr. Jiantao Yang is a leading researcher in the field of human–robot interaction and intelligent control systems, with a primary focus on adaptive neural network control and impedance regulation. His most-cited work, "Adaptive neural impedance control with extended state observer for human–robot interactions by output feedback through tracking differentiator" (2020, 8 citations), addresses a critical bottleneck in collaborative robotics: the high nonlinearity and disturbances that limit practical impedance control. Yang’s major contribution lies in developing a novel control framework that integrates neural networks with extended state observers and tracking differentiators, enabling robust, output-feedback-based human–robot cooperation without requiring full-state measurement. This approach significantly enhances safety and adaptability in physical human–robot interaction, paving the way for more intuitive and reliable robotic assistants in manufacturing, rehabilitation, and service applications. His work is notable for bridging theoretical control design with real-world engineering challenges, offering a practical solution to a long-standing problem in the field. With a growing citation impact, Dr. Yang continues to advance the frontiers of adaptive control and robotics, making his research essential reading for students and engineers working on next-generation collaborative systems.
Research Focus
Key Achievements
Top Papers
- 1