Papers
3
Total Citations
32
H-Index
3
About
Jian Jin is a robotics and control systems researcher whose work spans industrial robot control, mobile robotics, and precision calibration techniques. His most significant contribution lies in the development of robust control methodologies for complex robotic systems, most notably his 2018 work on cascade path-tracking control for networked industrial robots, which introduced a constrained iterative feedback tuning approach to overcome the persistent challenges of external disturbances and parametric uncertainties in manufacturing environments — a paper that has garnered 23 citations and stands as his most impactful contribution to date. Jin has also advanced the field of autonomous mobile robotics through his 2020 research on inverse decoupling-based direct yaw moment control for four-wheel independent steering platforms, addressing the longstanding problem of coupled sideslip and yaw dynamics. Complementing his control work, Jin has contributed practical solutions to robot calibration, proposing a cost-effective laser displacement sensor-based method for identifying kinematic errors in six-degree-of-freedom industrial robots. Across his research portfolio, Jin consistently bridges theoretical control design with real-world industrial applicability, making his work particularly relevant for engineers and researchers working at the intersection of precision manufacturing and autonomous robotics.
Research Focus
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