Shaoying He
Papers
8
Total Citations
116
H-Index
5
About
Shaoying He is a leading researcher at the intersection of robotics, control theory, and intelligent systems, with a primary focus on advanced control strategies for complex robotic platforms. His work centers on iterative learning control, visual servoing, and the modeling and control of rigid-soft hybrid robots. He is best known for pioneering data-driven compensation methods that overcome the conservatism of traditional robust control, as demonstrated in his highly cited 2021 work on iterative learning control (40 citations). He has made significant contributions to continuum robot control, developing an error-driven active disturbance rejection control (ADRC) approach with an input mapping method (2024, 28 citations) to address parametric uncertainties and disturbances inherent in deformable structures. His 2022 paper on eye-in-hand visual servoing (27 citations) introduced a novel input mapping method that effectively handles model inaccuracies. He has also advanced the field of hybrid hard-soft robots, proposing a unified modeling and data-driven control framework that balances accuracy with safe interaction. With a growing portfolio of work that bridges theoretical control design with practical robotic applications, He’s research is shaping the next generation of adaptive, robust, and intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Iterative Learning Control With Data-Driven-Based Compensation40 citations · 2021
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- 7A Robust Model Reconstruction Algorithm For Elevator Shaft2 citations · 2023
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