Ningyu Zhu
Papers
3
Total Citations
9
H-Index
2
About
Ningyu Zhu is a robotics researcher whose work focuses on the control and trajectory planning of complex robotic systems, particularly parallel robots and cooperative manipulators. Zhu’s major contributions lie in developing advanced control strategies to enhance precision and stability in highly nonlinear robotic environments. Their most cited paper, “Adaptive Sliding Mode Control with RBF Neural Network-Based Tuning Method for Parallel Robot” (2022, 5 citations), introduces a novel adaptive sliding mode control scheme that leverages radial basis function neural networks to improve trajectory tracking for 6-RSS parallel robots in Cartesian space—a critical advancement for applications requiring high accuracy. Additionally, Zhu has pioneered leader-follower trajectory planning approaches for cooperative robotic systems used in automated fiber placement (AFP), as detailed in two 2023 papers (each with 2 citations). These works address the challenge of coordinating multiple robots to ensure product quality in manufacturing. Zhu’s research bridges theoretical control methods with practical industrial applications, offering valuable insights for students and researchers in robotics, automation, and advanced manufacturing.
Research Focus
Key Achievements
Top Papers
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