Shiping Yang
Papers
4
Total Citations
53
H-Index
3
About
Shiping Yang is a researcher whose work bridges the frontiers of control theory, robotics, and intelligent manufacturing. His primary research areas include adaptive and iterative learning control for multi-agent systems, nonlinear discrete-time systems with periodic uncertainties, and the design of biomimetic robotic platforms. Yang’s most significant contribution is the development of an adaptive iterative learning control method to synchronize heterogeneous multi-agent systems, a foundational paper that has garnered 31 citations for addressing both parametric and non-parametric uncertainties in nonlinear dynamics. He further advanced the field with an adaptive backstepping repetitive learning control design for nonlinear discrete-time systems (16 citations), providing a robust solution for tracking problems under periodic uncertainties. Beyond theoretical control, Yang has demonstrated practical engineering impact by co-designing a biomimetic robotic fish capable of 3D locomotion, incorporating a “two-tanks” diving system for pitch control. Most recently, his work on keypoint recognition for industrial human-robot safe collaboration (2024) reflects a timely pivot toward Industry 5.0, focusing on human-centered intelligent manufacturing. With a career spanning foundational control theory to applied robotics, Yang’s research continues to shape how autonomous systems learn, synchronize, and safely interact with humans.
Research Focus
Key Achievements
Top Papers
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