Papers
18
Total Citations
779
H-Index
12
About
Yinyan Zhang is a prominent researcher specializing in recurrent neural networks, optimization-based robotics control, and distributed multi-agent systems. His work sits at the compelling intersection of computational intelligence and real-world robotic applications, with a particular focus on solving time-varying mathematical problems in dynamic environments. Zhang's most influential contribution, his 2016 paper on Modified Zeroing Neural Networks (ZNN) for time-varying quadratic programming, has garnered over 250 citations, addressing the critical challenge of noise-tolerant real-time optimization for robot manipulators—a problem with direct industrial relevance. His subsequent work on adaptive projection neural networks and tri-projection architectures further advanced redundancy resolution in manipulator control, demonstrating a sustained commitment to making robotic systems more capable and physically realistic. Beyond robotics, Zhang has made notable strides in distributed consensus algorithms, developing biased min-consensus frameworks for path planning in networked mobile robot systems. His explorations into Zhang Dynamics methodology for conquering control singularities reflect a theoretically rigorous approach that bridges pure mathematics and applied control engineering. With over 700 cumulative citations and active publications spanning nearly a decade, Zhang's research continues to shape how autonomous robotic systems perceive, plan, and act—making his work essential reading for students and researchers in intelligent control and multi-robot systems.
Research Focus
Key Achievements
Top Papers
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- 3Distributed Biased Min-Consensus With Applications to Shortest Path Planning96 citations · 2017
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- 6A recurrent neural network approach for visual servoing of manipulators29 citations · 2017
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- 9Tri-Projection Neural Network for Redundant Manipulators19 citations · 2022
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