Yanli Fan
Papers
5
Total Citations
58
H-Index
4
About
Yanli Fan is a leading researcher in advanced adaptive control for robotic systems, with a focused expertise in fixed-time and predefined-time control strategies. Her major contributions lie in developing neuro-adaptive and fuzzy-logic-based control frameworks that address critical challenges in robot manipulators, including dynamic uncertainty, input saturation, external disturbances, and constrained position errors. Fan’s work uniquely integrates composite learning and neural network techniques to achieve both rapid convergence and high tracking precision, while ensuring system stability and transient performance. Her most-cited papers, including those on fixed-time neuro-optimal control and predefined-time smooth control, have collectively garnered over 58 citations since 2022, reflecting their growing impact on the field. Notably, her 2024 studies on singularity-free switching and composite learning for manipulators represent significant advances in practical, robust control design. Fan’s research is highly relevant for students and engineers working on intelligent robotics, offering theoretically rigorous yet implementable solutions for uncertain and safety-critical systems.
Research Focus
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Top Papers
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