Pengxiao Jia
Papers
2
Total Citations
15
H-Index
2
About
Pengxiao Jia is a robotics researcher whose work focuses on the control and vibration suppression of flexible joint robots—a critical area for achieving high-precision, safe, and efficient automation. Jia’s major contributions center on developing advanced control strategies that overcome the inherent challenges of flexibility, such as trajectory tracking errors and residual vibration. In their most-cited work (2019, 8 citations), Jia proposed a novel controller that leverages motor state feedback and a dynamic surface approach, coupled with a state observer, to enable accurate trajectory tracking even when direct link state information is unavailable. This work is foundational for robots performing delicate or repetitive tasks. Earlier, Jia introduced an off-line learning input shaping method (2012, 7 citations) that effectively suppresses time-varying residual vibration in flexible joint robots during repetitive operations. By combining learning-based adaptation with classical input shaping, this approach offers a practical, robust solution for industrial applications. Jia’s research bridges theoretical control design and real-world implementation, making significant strides in enhancing the performance and reliability of flexible robotic systems.
Research Focus
Key Achievements
Top Papers
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