Papers

22

Total Citations

556

H-Index

11

About

Zhi-Wei Liu is a prominent researcher specializing in networked robotic systems, multi-agent coordination, and hierarchical control theory. His work sits at the intersection of control engineering, robotics, and networked systems, with a particular focus on solving complex coordination problems under realistic constraints such as communication limitations, disturbances, and system uncertainties. Liu's most significant contributions center on hierarchical control architectures for networked Euler-Lagrange and robotic systems. His highly cited 2019 paper (92 citations) introduced hierarchical controller-estimator algorithms to address coordination under sampled-data interactions and switching topologies, while subsequent work extended these frameworks to tackle bipartite tracking, formation control, and multi-target tracking across heterogeneous robot networks. His 2021 contribution on predefined-time stabilization (71 citations) further demonstrated his ability to advance fundamental control theory with practical implications. Collectively accumulating over 500 citations, Liu's research portfolio reveals a consistent drive to address real-world challenges including discrete communications, actuator faults, signed interaction graphs, and kinematic redundancy. His explorations of teleoperation, fuzzy fault-tolerant control, and prescribed-time extended state observers reflect both theoretical depth and applied vision. Liu's body of work makes him an essential reference for researchers navigating the rapidly evolving field of networked autonomous systems.

Research Focus

Key Achievements

11
H-Index
22
Papers
556
Total Citations
25
Avg Citations/Paper
🏆 Most Cited Paper
Hierarchical Controller-Estimator for Coordination of Networked Euler–Lagrange Systems
92 citations · 2019
📈 Most Prolific Year: 2020 (4 Papers)
🤝 Key Collaborators: 50
🏛 Institutions: Huazhong University of Science and Technology, Nanchang University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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