Papers
10
Total Citations
181
H-Index
8
About
Qing-Guo Wang is a versatile researcher whose work spans robotics, control systems, and intelligent automation, with contributions ranging from biologically inspired locomotion to advanced adaptive control theory. Perhaps his most recognized work lies in the development of central pattern generator (CPG) approaches for anguilliform robotic fish, where his 2013 paper garnered 58 citations by demonstrating how coupled Andronov-Hopf oscillators can generate naturalistic underwater locomotion — a meaningful advance over traditional CPG architectures. Complementing this, his motion library design and collision-free planning frameworks established a practical toolkit for biomimetic robotic navigation. Wang's contributions extend well into classical control foundations, including early work on Lagrangian system identifiability (1991) and parameter identification without acceleration sensing (1996), both addressing fundamental challenges in mechanical system modeling. More recently, his research has pivoted toward sophisticated adaptive fuzzy control, producing multiple 2023 papers on flexible-joint robots and robotic manipulators under constraints, faults, and dead-zones, each accumulating citations rapidly. His 2023 survey on transient performance control signals his continued influence in shaping the field's research agenda. Across decades and domains, Wang's body of work reflects a sustained commitment to bridging theoretical rigor with practical robotic applications.
Research Focus
Key Achievements
Top Papers
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- 3Survey of transient performance control20 citations · 2023
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- 8Identifiability of Lagrangian Systems With Application to Robot Manipulators11 citations · 1991
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- 10Collision-free motion planning for an Anguilliform robotic fish3 citations · 2012