Papers
8
Total Citations
124
H-Index
6
About
Shijie Song is a robotics and control systems researcher whose work sits at the intersection of parallel robot manipulation, intelligent control, and human-robot interaction. His research has made notable contributions to addressing one of the most persistent challenges in robotic systems: compensating for uncertain loads and unknown dynamics in high-precision platforms. Song's landmark 2021 study on modal space neural network compensation control for the Gough-Stewart robot—now approaching 40 citations—demonstrated how intelligent compensation strategies could overcome the dynamic coupling caused by uncertain payloads, a problem he further addressed through Extended Kalman Filter-based load parameter identification. His work spans both model-based and data-driven paradigms, including a deterministic approximate dynamic programming framework for optimal tracking in nonlinear systems with unknown dynamics. Song has also advanced visual servoing and admittance control for collaborative robots, integrating sliding mode techniques to handle actuator saturation and ensure safe, compliant physical interaction. His 2025 survey on compliant force control reflects his growing influence in synthesizing the field's state of knowledge. Collectively accumulating over 120 citations, Song's research offers both theoretical rigor and practical relevance for next-generation robotic systems.
Research Focus
Key Achievements
Top Papers
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- 5Design of a Gough–Stewart Platform Based on Visual Servoing Controller13 citations · 2022
- 6Compliant Force Control for Robots: A Survey10 citations · 2025
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