Qiuyue Zuo
Papers
8
Total Citations
82
H-Index
4
About
Qiuyue Zuo is an emerging researcher specializing in zeroing neural networks (ZNN), robotic control systems, and real-time dynamic problem-solving. Their work sits at the intersection of neural computation and robotics, with a particular focus on developing faster, more robust algorithms for time-varying mathematical problems with direct engineering applications. Zuo's most significant contributions center on advancing ZNN architectures to overcome longstanding limitations in convergence speed and noise resistance. Their predefined-time adaptive ZNN for solving time-varying linear equations, applied to UR5 robot systems, has garnered 25 citations since 2024, while their fixed-time robust ZNN model for manipulator redundancy resolution has attracted 23 citations in the same period. Earlier work on finite-time ZNN models for dynamic generalized Lyapunov equations, also with 23 citations, demonstrates a consistent trajectory of high-impact research since 2021. Beyond theoretical innovation, Zuo has extended these frameworks into practical domains including robotic arm pose tracking, wheeled unmanned ground vehicles, and minimally invasive surgical robotics with visual servo control. This breadth reveals a researcher committed to bridging mathematical rigor with real-world deployment, making Zuo's growing body of work essential reading for anyone working in intelligent robotic systems or neurodynamics-based computation.
Research Focus
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Top Papers
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