Mouquan Shen
Papers
3
Total Citations
51
H-Index
3
About
Mouquan Shen is a leading researcher in robust control theory, specializing in the analysis and synthesis of complex networked and switched systems. His work primarily addresses critical challenges in Markov jump systems and nonlinear dynamics, with a strong focus on ensuring system stability and performance under adverse conditions. A key contribution is the development of finite-time resilient control strategies for networked Markov switched nonlinear systems, a framework that effectively combats gain perturbations—a common and dangerous issue in real-world control implementation. This work, published in 2025, has already garnered 24 citations, underscoring its immediate impact. Shen has also made significant strides in observer-based control, notably by integrating quantized sliding mode techniques with H∞ performance for Markov jump systems, a 2018 paper that remains highly influential with 23 citations. Further demonstrating his versatility, he has applied reinforcement learning, specifically Q-learning, to solve adaptive formation tracking problems for multi-mobile robot systems, showcasing a bridge between classical robust control and modern learning-based approaches. Through these contributions, Shen has established himself as a key figure advancing the reliability and intelligence of modern control systems.
Research Focus
Key Achievements
Top Papers
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