Papers
29
Total Citations
1,095
H-Index
13
About
Min Wang is a distinguished robotics and control systems researcher whose work sits at the intersection of adaptive neural control, robot learning, and human-robot interaction. His most influential contributions center on dynamic learning frameworks for robotic manipulators, particularly advancing adaptive neural control (ANC) with prescribed performance guarantees for nonlinear systems subject to unknown dynamics and external disturbances. His 2017 paper on dynamic learning from ANC for robot manipulators has garnered 271 citations, establishing him as a leading voice in intelligent robot control theory. Wang has made significant strides in robot skill acquisition, developing biologically inspired frameworks that enable robots to generalize complex manipulation skills learned from human demonstrations — work that has collectively attracted hundreds of citations. His research spans decentralized cooperative manipulator control, neuro-adaptive observer design for flexible joint robots, and teleoperation enhancement using hybrid control strategies. More recently, he has extended his expertise to parallel robot dynamics and autonomous task learning for telerobotics. Across ten highly cited works accumulating nearly 1,000 citations, Wang's research consistently bridges rigorous mathematical control theory with practical robotics applications, making meaningful contributions to autonomous systems, human-robot collaboration, and intelligent manufacturing.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Learning Framework of Adaptive Manipulative Skills From Human to Robot174 citations · 2018
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- 5Neuro-adaptive observer based control of flexible joint robot102 citations · 2017
- 6Enhanced teleoperation performance using hybrid control and virtual fixture48 citations · 2019
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- 10A Task Learning Mechanism for the Telerobots28 citations · 2019