Lecturer at Liaoning University of Technology, China
Ph.D. in Control Science and Engineering from Northeastern University (2023), Dr. Xinfeng Shao is a Lecturer & Master’s Supervisor at Liaoning University of Technology (LUT). His research focuses on adaptive intelligent control, cyber-physical system security, event-triggered control, and fault-tolerant control. He has led multiple national-level projects, including the National Youth Science Foundation Project, and contributed to 10+ papers in top journals, with two highly cited in IEEE Transactions on Fuzzy Systems. 📝 His technical skills include MATLAB/Simulink and Python, and he is an active member of the Chinese Association of Automation and IEEE. 🌐
Publication Profile
Orcid
🎓 Educational Background
Dr. Xinfeng Shao earned his Ph.D. in Control Science and Engineering from Northeastern University in 2023. His research is focused on adaptive intelligent control, cyber-physical system security, event-triggered control, and fault-tolerant control. These areas are critical for enhancing the reliability and security of modern automation systems, especially in the context of cyber-physical systems (CPS). His innovative work aims to develop intelligent control frameworks that ensure optimal performance even under uncertain and adversarial conditions. Dr. Shao’s expertise contributes significantly to the advancement of automation and security technologies.
🔬 Research Projects
Dr. Xinfeng Shao has been a Principal Investigator for several key research initiatives, including the National Youth Science Foundation Project (Category C), the Liaoning Provincial Education Department Youth Cultivation Project, and the LUT Doctoral Research Startup Fund. These projects reflect his leadership in advancing research in control systems and cybersecurity. Additionally, Dr. Shao is a Key Participant in two National Natural Science Foundation of China (NSFC) Projects and the prestigious Liaoning “Xing Liao Talent Program” Leading Talent Project. His involvement in these projects highlights his significant contribution to the scientific community.
📚 Research Achievements
Dr. Xinfeng Shao has authored over 10 papers in prestigious journals like IEEE Transactions, with 2 papers being ESI Top 1% Highly Cited. His research has introduced novel control frameworks for ensuring the security of multi-agent systems (MASs) and improving nonlinear fault tolerance. These groundbreaking contributions are recognized internationally for advancing the fields of adaptive control and cybersecurity. His work continues to shape the future of control systems and automation, making a significant impact in both theoretical and applied research.
🧠 Research Focus
Dr. Xinfeng Shao’s research is centered around adaptive intelligent control, cyber-physical system (CPS) security, fault-tolerant control, and event-triggered control. His work explores the resilience and security of multi-agent systems (MASs), focusing on attack-resistant control frameworks in the presence of malicious disturbances like FDI attacks and DoS attacks. Dr. Shao also investigates nonlinear systems and distributed control strategies, developing secure formation control and adaptive fuzzy control techniques. His contributions are instrumental in enhancing the robustness and security of automated systems, improving fault tolerance and performance under uncertain conditions
Conclusion
Mr. Xinfeng Shao is highly suitable for the Research for Best Researcher Award. His innovative research in intelligent and secure control systems, impressive publication record, project leadership, and recognized academic contributions present a strong case for this honor. He exemplifies the qualities of a top-tier researcher advancing critical technologies in automation and cyber-physical security.
📚 Publications Top Notes
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“Dynamic‐Event‐Based Predefined‐Time Secure Formation Control for Nonlinear Multiagent Systems Against FDI Attacks” – International Journal of Robust and Nonlinear Control, 2025. DOI: 10.1002/rnc.8017 🔒
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“Adaptive Fault-Tolerant Consensus Tracking Control of Stochastic High-Order MASs Under FDI Attacks” – IEEE Transactions on Fuzzy Systems, 2024. DOI: 10.1109/TFUZZ.2024.3352076 🔄
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“Event-based distributed resilient control strategy for microgrids subject to disturbances and hybrid attacks” – Applied Mathematics and Computation, 2023. DOI: 10.1016/j.amc.2023.128273 🌍
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“Robust adaptive dynamic memory‐event‐triggered attitude control for nonlinear multi‐UAVs resist actuator hysteresis” – International Journal of Robust and Nonlinear Control, 2023. DOI: 10.1002/rnc.6821 🚁
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“Event-based adaptive fuzzy fixed-time control for nonlinear interconnected systems with non-affine nonlinear faults” – Fuzzy Sets and Systems, 2022. DOI: 10.1016/j.fss.2021.08.005 ⚙️
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“Neural-network-based adaptive secure control for nonstrict-feedback nonlinear interconnected systems under DoS attacks” – Neurocomputing, 2021. DOI: 10.1016/j.neucom.2021.03.087 🧠
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“Fuzzy Adaptive Event-Triggered Secure Control for Stochastic Nonlinear High-Order MASs Subject to DoS Attacks and Actuator Faults” – IEEE Transactions on Fuzzy Systems, 2020. DOI: 10.1109/tfuzz.2020.3028657 🛡️
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“Adaptive Fuzzy Prescribed Performance Control of Non-Triangular Structure Nonlinear Systems” – IEEE Transactions on Fuzzy Systems, 2019. DOI: 10.1109/tfuzz.2019.2937046 🔧
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“Adaptive Fuzzy Prescribed Performance Control for MIMO Stochastic Nonlinear Systems” – IEEE Access, 2018. DOI: 10.1109/ACCESS.2018.2882634 🔄
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“Adaptive prescribed performance decentralized control for stochastic nonlinear large-scale systems” – International Journal of Adaptive Control and Signal Processing, 2018. 📊