Hsing‐Yi Chen
Papers
3
Total Citations
20
H-Index
3
About
Hsing‑Yi Chen is an emerging leader in intelligent control systems and multi‑robot coordination, with a focus on reinforcement learning and broad learning networks for autonomous mobile robots. Their research addresses critical challenges in formation control, collision avoidance, and trajectory tracking for omnidirectional and mecanum‑wheeled platforms. Chen’s most cited work, “Adaptive Reinforcement Learning Formation Control Using ORFBLS for Omnidirectional Mobile Multi‑Robots” (2023, 9 citations), introduces an adaptive reinforcement learning framework integrated with an output recurrent fuzzy broad learning system to achieve stable, decentralized formation control. This is complemented by “Intelligent Actor‑Critic Learning Control for Collision‑Free Trajectory Tracking of Mecanum‑Wheeled Mobile Robots” (2024, 7 citations), which applies actor‑critic methods to ensure safe navigation in dynamic environments. A notable achievement is their 2024 paper on ball‑riding robots, which presents a novel cyber‑physical approach combining output recurrent broad learning with backstepping sliding mode formation control for industrial cyber‑physical systems. With a growing citation record and a clear trajectory toward real‑world deployment, Chen’s work is shaping the next generation of adaptive, learning‑based multi‑robot systems.
Research Focus
Key Achievements
Top Papers
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