Shida Liu
Papers
4
Total Citations
17
H-Index
2
About
Shida Liu is a rising researcher in intelligent control systems and robotics, with a focus on model-free adaptive control (MFAC) and fault detection for collaborative robots. Their major contributions include developing a data learning-based MFAC method that integrates locally weighted regression and lazy learning, successfully applied to path-tracking control for an NAO robot—a work that has garnered 11 citations. Liu has also advanced control theory by proposing an improved MFAC algorithm with data compensation to handle time delay and data dropout in wheeled robots, and has innovated in industrial safety through a machine learning framework for multimodal fault detection in UR3 collaborative robots. Additionally, Liu has tackled practical challenges in autonomous navigation with an enhanced GC-RANSAC approach for ground segmentation in complex subway environments. With a growing citation record and publications spanning from 2022 to 2025, Liu’s work bridges theoretical control methods and real-world robotic applications, demonstrating significant potential for impact in both academic research and industrial deployment.
Research Focus
Key Achievements
Top Papers
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- 4An Enhanced GC-RANSAC Approach for Subway Indoor Ground Segmentation1 citations · 2024