Shenyang Aerospace University, China
Dong Liu is an Associate Professor at Shenyang Aerospace University’s College of Automation , holding a Ph.D. in control theory from Northeastern University (2018), recognized as a High-Level Talent by Shenyang Municipality , and serves as Deputy Director of the Liaoning Provincial Key Lab of Advanced Flight Control and Simulation ; he also actively contributes to national technical committees on data-driven control, intelligent systems, and motion control , with research spanning model-free adaptive control, secure systems, and aerospace applications .
Profile
Orcid
Scopus
🎓 Education
Dong Liu earned his Ph.D. in Control Theory and Control Engineering from Northeastern University in 2018 , where he focused on advanced control methodologies, model-free systems, and adaptive control technologies ; his academic training combined theoretical depth with practical relevance, particularly for aerospace and cyber-physical applications , laying a strong foundation for his current research in intelligent systems, data-driven strategies, and secure automation frameworks .
💼 Experience
Dr. Liu serves as Associate Professor at Shenyang Aerospace University , with leadership as Deputy Director of a provincial key laboratory in flight control and simulation ; his concurrent roles include memberships in top technical committees within Chinese automation, AI, and control systems communities , contributing deeply to cutting-edge projects in data-driven control, adaptive methods, and intelligent detection technologies across academic, military, and industrial sectors .
🏆 Awards and Honors
Dong Liu is recognized as a High-Level Talent by Shenyang Municipality , highlighting his contributions to advanced control systems and intelligent automation ; he holds a key leadership role in the Liaoning Provincial Lab for Flight Control and serves on three prestigious national-level technical committees , reflecting his influence in data-driven control, AI-based automation, and system intelligence development across China and internationally .
🔬 Research Focus
Dr. Liu’s research centers on data-driven control , model-free adaptive control , and secure control systems , emphasizing prescribed performance, sliding mode methods, and reinforcement learning in discrete-time nonlinear systems ; his work finds applications in aerospace , cyber-physical systems, and intelligent automation, with a strong emphasis on real-time response, robustness, and resilience against cyber threats such as DoS attacks .
✅ Conclusion
Dr. Dong Liu is highly suitable for the Best Researcher Award, given his strong theoretical contributions, real-world applications, and leadership in advanced control systems research. His consistent publication in high-impact journals, involvement in national research leadership, and work on critical topics such as data-driven and secure control for aerospace applications, position him as a leading researcher in his field. With strategic international engagement and interdisciplinary expansion, he is poised to become a globally recognized authority. His credentials make him not just a deserving candidate—but a standout contender for this prestigious recognition.
Publication
Author: Yijie Yang, Dong Liu, Xin Wang, Zhujun Wang
Title: Data-driven second-order iterative sliding mode control for cyber–physical systems under prescribed performance and DoS attacks
Year: 2025
Citation: Journal of Process Control, DOI: 10.1016/j.jprocont.2025.103422
Author: Dong Liu, Yi-Jie Yang, Li-Ying Hao
Title: Prescribed performance based data-driven adaptive sliding mode control for discrete-time nonlinear systems
Year: 2024
Citation: Journal of the Franklin Institute, DOI: 10.1016/j.jfranklin.2024.01.021
Author: Dong Liu, Zhi-Peng Zhou, Tie-Shan Li
Title: Data-Driven Bipartite Consensus Tracking for Nonlinear Multiagent Systems With Prescribed Performance
Year: 2023
Citation: IEEE Transactions on Systems, Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2022.3230504
Author: Dong Liu, Ning Liu, Tie-Shan Li
Title: Event-triggered model-free adaptive control for nonlinear systems with output saturation
Year: 2023
Citation: International Journal of Robust and Nonlinear Control, DOI: 10.1002/rnc.6747
Author: Dong Liu, Guang-Hong Yang
Title: Data-Driven Adaptive Sliding Mode Control of Nonlinear Discrete-Time Systems With Prescribed Performance
Year: 2019
Citation: IEEE Transactions on Systems, Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2017.2779564
Author: Dong Liu, Guang-Hong Yang
Title: Prescribed Performance Model-Free Adaptive Integral Sliding Mode Control for Discrete-Time Nonlinear Systems
Year: 2019
Citation: IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2018.2881205
Author: Dong Liu
Title: Model-Free Adaptive Control Design for Nonlinear Discrete-time Processes with Reinforcement Learning Techniques
Year: 2018
Citation: International Journal of Systems Science
Author: Dong Liu
Title: Performance-based data-driven model-free adaptive sliding mode control for a class of discrete-time nonlinear processes
Year: 2018
Citation: Journal of Process Control
Author: Dong Liu
Title: Data-Driven Adaptive Sliding Mode Control of Nonlinear Discrete-Time Systems With Prescribed Performance
Year: 2017
Citation: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Author: Dong Liu
Title: Event-based model-free adaptive control for discrete-time non-linear processes
Year: 2017
Citation: IET Control Theory & Applications