Papers
5
Total Citations
23
H-Index
3
About
Qiancheng Li is a robotics researcher whose work sits at the intersection of precision control, parallel mechanisms, and soft robotics. His primary contributions focus on trajectory tracking for high-speed parallel robots, particularly SCARA configurations used in repetitive pick-and-place operations. Li has pioneered the integration of fuzzy adaptive iterative learning control with closed-loop feedback, achieving enhanced disturbance rejection and high-precision motion despite modeling uncertainties—a critical challenge in industrial automation. His most cited works (each with 6 citations) include a 2023 study on fuzzy adaptive iterative learning control for fast parallel SCARA robots and a 2022 paper on open-closed-loop iterative learning control for a 4-DOF parallel robot. Beyond rigid robots, Li has also advanced soft robotics through a novel ball joint with continuously adjustable load capacity based on positive pressure friction, and developed an accurate position acquisition method for hyper-redundant arms under load using flexible sensors. His research demonstrates a systematic approach to overcoming real-world control limitations, from factory floors to flexible manipulators.
Research Focus
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