Mr. Yuhang Meng | Control Awards | Best Researcher Award
Mr. Yuhang Meng | Control Awards | Nanjing University of Science and Technology | China
Mr. Yuhang Meng is a highly motivated and accomplished researcher in the field of Electronic Information, specializing in advanced control systems, fault-tolerant mechanisms, and unmanned vehicle technologies, with a growing record of impactful publications and international recognition. He received his Bachelor’s degree in Electrical Engineering from Suzhou City University, followed by a Master’s degree in Electronic Information from Jiangsu University of Science and Technology, and is currently pursuing his EngD in Electronic Information at Nanjing University of Science and Technology under the supervision of Professor Zhengrong Xiang. Throughout his academic career, Mr. Meng has gained extensive experience in switched systems, adaptive control, sliding mode control, and the development of advanced algorithms for autonomous systems, with a specific emphasis on unmanned surface and amphibious vehicles. His professional experience reflects active engagement in high-impact research projects, both theoretical and application-oriented, resulting in publications in leading international journals such as IEEE Transactions on Mechatronics, IEEE Transactions on Industrial Electronics, Aerospace Science and Technology, and Applied Ocean Research, many of which are indexed in Scopus and widely cited within the research community. His expertise extends to designing robust trajectory-tracking controllers, developing hybrid amphibious platforms, and implementing artificial intelligence-based approaches such as bidirectional long short-term memory neural networks for adaptive control
Professional Profile: ORCID
Selected Publications
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Design and Analysis of a Multimodal Hybrid Amphibious Vehicle, 2025 – Citations: 15
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Trajectory tracking control for unmanned amphibious surface vehicles with actuator faults, 2024 – Citations: 22
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An adaptive internal model control approach for unmanned surface vehicle based on bidirectional long short-term memory neural network: Implementation and field testing, 2024 – Citations: 18
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Trajectory‐tracking control of an unmanned surface vehicle based on characteristic modelling approach: Implementation and field testing, 2023 – Citations: 12