Viet-Hung Vu
Papers
10
Total Citations
142
H-Index
6
About
Viet-Hung Vu is a researcher specializing in structural dynamics, modal analysis, and the mechanics of flexible robotic systems. His work bridges theoretical modeling and practical applications, with a particular focus on developing advanced signal processing and system identification techniques for complex, time-varying mechanical structures. Vu's most significant contributions lie in the identification of modal parameters for flexible and dynamic systems. His highly cited 2017 paper on Data-Driven Stochastic Subspace Identification (27 citations) introduced improved methods for distinguishing physical modal parameters from computational noise in time-varying systems — a critical challenge in real-world structural monitoring. Complementing this, his 2021 work on linearizing dynamic equations for flexible-joint manipulators (34 citations) provided essential tools for vibration and modal analysis in robotics, while a Kalman filter-based ARX modeling approach for force identification (26 citations) demonstrated his capacity to integrate estimation theory with structural dynamics. A recurring theme throughout Vu's research is robotic grinding — a technically demanding application where flexible manipulators must maintain precision under dynamic loading. His development of symbolic differentiation algorithms for flexible-joint inverse dynamics further underscores his commitment to computationally efficient, real-time-capable solutions. With nearly 140 total citations, Vu's contributions have meaningfully advanced the field of operational modal analysis and flexible robotics.
Research Focus
Key Achievements
Top Papers
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- 8Online modal analysis of a flexible robot during grinding4 citations · 2011
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