Papers
4
Total Citations
37
H-Index
3
About
Shengli Du is a rising leader in the intersection of multi-agent systems, swarm robotics, and intelligent control. His research focuses on solving critical coordination challenges, particularly target tracking in dynamic and adversarial environments. Du’s major contributions include developing a **real-time local path planning strategy based on deep distributional reinforcement learning** (2024, 25 citations), which enables autonomous systems to make rapid, adaptive decisions under uncertainty. He has also pioneered **fully distributed fixed-time control for cross-domain swarm robots** tracking non-cooperative targets (2023), addressing the dual challenges of hostile target behavior and heterogeneous robot collaboration. His earlier work on **adaptive sliding mode control** (2020) tackled target tracking under continuously time-varying topologies and external disturbances, while his most recent research (2025) extends Nash equilibrium seeking to high-order multi-agent systems with unknown disturbances. With a growing citation footprint and a clear trajectory toward robust, scalable, and intelligent swarm coordination, Du’s work is foundational for next-generation autonomous systems in defense, disaster response, and multi-robot collaboration.
Research Focus
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