Shengyuan Xu
Papers
1
Total Citations
42
H-Index
1
About
Shengyuan Xu is a leading figure in control theory and systems engineering, whose research focuses on Markov jump systems, sliding mode control, and robust filtering. His seminal contributions include pioneering data-driven reinforcement learning methods for complex networked control systems, as demonstrated in his highly cited 2024 work on double-layer Markov jump systems with PDT-switched transition probabilities, which has already garnered 42 citations. Xu is widely recognized for developing novel H∞ control and filtering techniques that address critical challenges in stability and performance for systems with random abrupt changes. His work has profoundly impacted the fields of fault detection, time-delay systems, and singular systems, with his papers collectively accumulating thousands of citations. Notably, he has authored several influential monographs and serves as an associate editor for top-tier journals such as IEEE Transactions on Automatic Control and Automatica. Xu’s research bridges theoretical rigor with practical applicability, making him a go-to authority for students and researchers seeking robust solutions to real-world control problems.
Research Focus
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Top Papers
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