Boyu Zheng
Papers
7
Total Citations
52
H-Index
5
About
Boyu Zheng is a rising researcher in the fields of robotics, neural networks, and control theory, with a focus on advancing the motion control and coordination of robotic manipulators. His work centers on developing novel recurrent neural networks (RNNs), particularly zeroing neural networks (ZNNs), to solve complex time-varying problems in noisy and finite-energy environments. Zheng’s major contributions include the introduction of variable-parameter and predefined-time convergence neural networks that address critical issues such as joint-angle drift in redundant robot arms and cooperative motion in dual-arm systems. His most-cited paper, a 2023 study on a varying-parameter periodic rhythm neural network for solving time-varying matrix equations, has garnered 12 citations, while his 2024 work on a finite-time neural network for joint-angle drift has received 10 citations. Notably, his research extends to applications in UR3 robotic arm control and multiagent systems, demonstrating practical impact. With over 50 citations across his top papers, Zheng is establishing himself as an innovator in noise-tolerant, convergence-guaranteed neural solutions for real-world robotics.
Research Focus
Key Achievements
Top Papers
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