Yakun Zhao
Papers
10
Total Citations
334
H-Index
7
About
Yakun Zhao is a robotics and control systems researcher whose work spans two interconnected frontiers: multi-agent formation control and tethered space robotics. His most widely cited contribution, "Distributed Formation Control Using Artificial Potentials and Neural Network for Constrained Multiagent Systems" (2018, 110 citations), introduced a novel integration of radial basis function neural networks with artificial potential fields to address complex multi-agent coordination under real-world constraints — a foundational advance for autonomous swarm systems. Alongside this, Zhao has established himself as a leading voice in the niche but critically important field of tethered space net robots (TSNR), developing comprehensive frameworks for capturing and removing orbital debris. His research tackles the full pipeline of this challenge — from dynamic modeling and contact mechanics to Super-Twisting Sliding Mode Control strategies — with papers accumulating over 150 citations collectively across the series. Notably, his 2017 work on Super-Twisting Sliding Mode Control for tethered space systems (57 citations) laid key groundwork for subsequent studies on impulsive control and dynamic closing point determination. Zhao's research addresses one of space exploration's most pressing concerns: sustainable orbital environments, making his contributions both technically rigorous and globally relevant.
Research Focus
Key Achievements
Top Papers
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- 4Contact Dynamics and Control for Tethered Space Net Robot37 citations · 2018
- 5Robust distributed consensus for deployment of Tethered Space Net Robot30 citations · 2018
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- 9Consensus for Multi-agent Systems in the Presence of Attitude Constraint2 citations · 2018
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