Junnan Xie
Papers
1
Total Citations
3
H-Index
1
About
Junnan Xie is a researcher focused on the modeling and control of continuum robots, particularly for applications in rescue and medical environments. Their work addresses the critical challenge of managing modeling uncertainties in these flexible, tendon-driven systems. Xie’s most-cited paper, "Neural Network Adaptive Tracking Control for Continuum Robots Considering Modeling Uncertainties" (2022), introduces a dynamic model based on the Euler-Bernoulli beam equation under the constant curvature assumption, combined with a neural network adaptive controller to enhance tracking precision. This contribution has garnered 3 citations, laying a foundation for robust control in soft robotics. By integrating structural mechanics with adaptive learning, Xie’s research advances the reliability of continuum robots in complex, unstructured settings—a key step toward safer surgical tools and more agile rescue robots. Their work exemplifies the intersection of theoretical modeling and practical control, offering valuable insights for students and engineers exploring nonlinear dynamics and intelligent systems in robotics.
Research Focus
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Top Papers
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