Papers
9
Total Citations
136
H-Index
6
About
Shenquan Wang is a pioneering researcher at the intersection of robotics, control theory, and intelligent systems, with a focus on adaptive control for robotic manipulators and autonomous systems in challenging environments. His most impactful work, "Coal resources under carbon peak: Segmentation of massive laser point clouds for coal mining in underground dusty environments using integrated graph deep learning model" (2023, 78 citations), demonstrates his ability to apply advanced AI and graph-based deep learning to real-world industrial challenges, specifically improving safety and efficiency in coal mining. Wang’s core contributions lie in prescribed-time and fixed-time fuzzy adaptive control, where he has developed novel frameworks to eliminate dependence on initial conditions and controller parameters—a significant advancement over traditional finite/fixed-time methods. His work on human-robot co-transportation (2024, 8 citations) addresses critical safety constraints, including velocity observation and obstacle avoidance, while his self-triggered quantized control strategies (2024, 7 citations) enhance communication efficiency in networked systems. With over 130 citations across his top papers, Wang has also tackled decentralized robust optimal control for modular robots and trajectory tracking for mobile robots. His research is notable for bridging theoretical rigor with practical applications, making him a key figure in advancing secure, adaptive, and optimal control for next-generation robotic systems.
Research Focus
Key Achievements
Top Papers
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