Papers
11
Total Citations
279
H-Index
9
About
Mingsheng Shang is a leading researcher in multi-robot coordination and intelligent control, with a focus on neural-network-driven solutions for complex robotic systems. His major contributions lie in developing distributed competition strategies for multi-robot coordination under variable and switching topologies, where he pioneered the use of winner-take-all (WTA) and k-winners-take-all (kWTA) neural networks to enable efficient, decentralized decision-making. His work on gradient-based differential kWTA networks has been particularly influential, with his most-cited paper (62 citations) establishing a foundation for competitive multi-robot behavior. Shang has also made significant advances in rehabilitation robotics, creating projected recurrent neural networks for lower limb movement intention recognition (33 citations), and in redundant manipulator control, where his dynamic neural network approaches address orientation tracking and bicriteria optimization (26 and 25 citations, respectively). His research extends to noise-tolerant zeroing neural networks for distributed motion planning, demonstrating robustness in real-world applications. With over 270 citations across his top ten papers, Shang’s work is shaping the future of autonomous multi-robot systems and human-robot interaction.
Research Focus
Key Achievements
Top Papers
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- 5Dynamic Neural Network for Bicriteria Weighted Control of Robot Manipulators25 citations · 2021
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