Jiqian Xu
Papers
8
Total Citations
100
H-Index
5
About
Jiqian Xu is a leading researcher in robotics and intelligent control systems, with a primary focus on advanced control strategies for robotic manipulators. His work addresses critical challenges in robot dynamics, including model-free control, backlash compensation, and precision positioning. Xu’s most impactful contribution is the development of a cross-modal attention fusion network for RGB-D semantic segmentation (2023, 44 citations), which advances computer vision for robotic perception. He has made significant strides in adaptive sliding mode control, proposing a model-free approach with adjustable funnel boundaries (2021, 19 citations) that eliminates the need for nominal dynamics models—a breakthrough for real-world applications. His research on one-step identification of robot physical dynamic parameters (2024, 9 citations) introduces a velocity-load friction model to enhance torque accuracy. Xu also pioneered dual-motor anti-backlash control for 7-DOF robotic manipulators (2023, 8 citations), improving precision in over-actuated systems. His work on funnel-based sliding mode control (2020, 9 citations) and finite-time prescribed performance control (2024) further demonstrates his expertise in handling dynamic uncertainties and disturbances. With over 100 total citations, Xu’s contributions are shaping the next generation of robust, adaptive, and perceptive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Cross-modal attention fusion network for RGB-D semantic segmentation44 citations · 2023
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