Papers
7
Total Citations
171
H-Index
4
About
Dongyu Li is a robotics researcher whose work spans adaptive control, neural networks, and intelligent systems for robotic manipulation. His key research areas include adaptive neural network control, space robotics, and the integration of knowledge graphs with medical AI. Li’s most significant contribution is his 2021 paper on adaptive bias radial basis function neural network (RBFNN) control for robotic manipulators, which has garnered 116 citations—demonstrating its substantial impact on the field. In this work, he addressed critical limitations of traditional RBFNNs by proposing a composite adaptive control scheme with optimized hidden node distribution, overcoming inherent demerits like difficult approximation domain determination. Li has also made notable strides in space robotics, designing a novel motor structure with an extended particle swarm optimization algorithm for improved control of space robots, and exploring model predictive manipulation for compliant objects with occlusion compensation. His interdisciplinary work extends to medical AI, where he developed a knowledge graph-based automatic question answering system. With a historical and futuristic perspective on robotics, Li continues to push boundaries in adaptive control and intelligent automation.
Research Focus
Key Achievements
Top Papers
- 1Adaptive bias RBF neural network control for a robotic manipulator116 citations · 2021
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- 3Social Robotics9 citations · 2021
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- 7Historical and futuristic perspectives of robotics2 citations · 2020