Dong Liu | Data Driven Control | Best Researcher Award

Prof. Dong Liu | Data Driven Control | Best Researcher Award

Deputy Director of Provincial Key Laboratory at Shenyang Aerospace University, China

Dong Liu is an Associate Professor at the College of Automation, Shenyang Aerospace University, China. He is a leading researcher in the field of control theory and engineering, especially recognized for his work on data-driven control, model-free adaptive control, and secure control systems. He plays active roles in various national technical committees related to automation, command and control, and artificial intelligence.

Publication ProfileΒ 

Scopus

Orcid

Educational Background πŸŽ“

  • Ph.D. in Control Theory and Control Engineering,
    Northeastern University, China β€” 2018

Professional Experience πŸ’Ό

  • Associate Professor,
    College of Automation, Shenyang Aerospace University

  • Deputy Director,
    Liaoning Provincial Key Laboratory of Advanced Flight Control and Simulation Technology

  • Recognized as a High-Level Talent by Shenyang Municipality

Committee Roles:

  • Committee Member, Technical Committee on Data-Driven Control, Learning, and Optimization (Chinese Association of Automation)

  • Committee Member, Technical Committee on Intelligent Control and Systems (Chinese Command and Control Association)

  • Committee Member, Technical Committee on Intelligent Detection and Motion Control Technology (Chinese Association for Artificial Intelligence)

Research Interests πŸ”¬

  • Data-Driven Control

  • Model-Free Adaptive Control (MFAC)

  • Secure Control Systems

  • Sliding Mode Control

  • Cyber-Physical Systems

  • Prescribed Performance Control

  • Aerospace Control Applications

  • Event-Triggered and Reinforcement Learning-based Control

Awards and HonorsπŸ†βœ¨

  • Recognized as High-Level Talent by the Shenyang Municipality

  • Appointed Deputy Director of a Provincial Key Laboratory

  • Holds Multiple Technical Committee Memberships in prestigious national associations

Conclusion🌟

Dr. Dong Liu is an influential scholar in control engineering, especially in data-driven and model-free adaptive control systems with an emphasis on robustness, security, and real-time applications in aerospace and cyber-physical systems. His high-impact journal publications and leadership roles in national technical committees underline his strong academic and technical contributions. He is actively shaping the future of intelligent control in China and internationally.

Publications πŸ“š

πŸ“˜ Data-driven second-order iterative sliding mode control for cyber–physical systems under prescribed performance and DoS attacks
✍️ Yijie Yang, Dong Liu, Xin Wang, Zhujun Wang
πŸ—žοΈ Journal of Process Control, June 2025
πŸ”— DOI: 10.1016/j.jprocont.2025.103422


πŸ“˜ Prescribed performance based data-driven adaptive sliding mode control for discrete-time nonlinear systems
✍️ Dong Liu, Yi-Jie Yang, Li-Ying Hao
πŸ—žοΈ Journal of the Franklin Institute, March 2024
πŸ”— DOI: 10.1016/j.jfranklin.2024.01.021


πŸ“˜ Data-Driven Bipartite Consensus Tracking for Nonlinear Multiagent Systems With Prescribed Performance
✍️ Dong Liu, Zhi-Peng Zhou, Tie-Shan Li
πŸ—žοΈ IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2023
πŸ”— DOI: 10.1109/TSMC.2022.3230504


πŸ“˜ Event‐triggered model‐free adaptive control for nonlinear systems with output saturation
✍️ Dong Liu, Ning Liu, Tieshan Li
πŸ—žοΈ Int. Journal of Robust and Nonlinear Control, August 2023
πŸ”— DOI: 10.1002/rnc.6747


πŸ“˜ Data-Driven Adaptive Sliding Mode Control of Nonlinear Discrete-Time Systems With Prescribed Performance
✍️ Dong Liu, Guang-Hong Yang
πŸ—žοΈ IEEE Trans. on Systems, Man, and Cybernetics: Systems, December 2019
πŸ”— DOI: 10.1109/TSMC.2017.2779564


πŸ“˜ Prescribed Performance Model-Free Adaptive Integral Sliding Mode Control for Discrete-Time Nonlinear Systems
✍️ Dong Liu, Guang-Hong Yang
πŸ—žοΈ IEEE Trans. on Neural Networks and Learning Systems, July 2019
πŸ”— DOI: 10.1109/TNNLS.2018.2881205


πŸ“˜ Model-Free Adaptive Control Design for Nonlinear Discrete-time Processes with Reinforcement Learning Techniques
✍️ Dong Liu
πŸ—žοΈ International Journal of Systems Science, July 2018
πŸ”— [Link not provided]


πŸ“˜ Performance-based data-driven model-free adaptive sliding mode control for a class of discrete-time nonlinear processes
✍️ Dong Liu
πŸ—žοΈ Journal of Process Control, June 2018
πŸ”— [Link not provided]


πŸ“˜ Data-Driven Adaptive Sliding Mode Control of Nonlinear Discrete-Time Systems With Prescribed Performance
✍️ Dong Liu
πŸ—žοΈ IEEE Trans. on Systems, Man, and Cybernetics: Systems, November 2017
πŸ”— [Link not provided]


πŸ“˜ Event-based model-free adaptive control for discrete-time non-linear processes
✍️ Dong Liu
πŸ—žοΈ IET Control Theory & Applications, September 2017
πŸ”— [Link not provided]


πŸ“˜ Neural network-based event-triggered MFAC for nonlinear discrete-time processes
✍️ Dong Liu
πŸ—žοΈ Neurocomputing, July 2017
πŸ”— [Link not provided]


Chao Zhang | Machine Learning | Best Researcher Award

Prof. Chao Zhang | Machine Learning | Best Researcher Award

Professor at Shanghai University, China

Professor Chao Zhang is a distinguished academic and researcher specializing in mechanical engineering, particularly in tribology and engine component wear. With an extensive career spanning multiple prestigious institutions, including Shanghai University and Northwestern University, he has significantly contributed to the field through research, publications, and technical committee roles. His expertise integrates classical tribology with modern computational techniques such as machine learning and quantum chemical molecular dynamics.

Publication ProfileΒ 

Scopus

Educational Background πŸŽ“

  • Bachelor’s Degree: Mechanical Engineering, Shanghai Railway University (1983)

  • Master’s Degree: Mechanical Engineering, Shanghai Internal Combustion Engine Research Institute (1989)

  • Ph.D.: Mechanical Engineering, Shanghai University (1997)

Professional Experience πŸ’Ό

  • Senior Research Associate (1997–2002): Northwestern University, USA (Worked with Profs. H.S. Cheng and Qian Wang)

  • Professor:

    • Tongji University, China

    • Shanghai University, China

    • Kunming University of Science and Technology, China

  • Technical Committee Member: Engines and Powertrains, International Federation for the Promotion of Mechanism and Machine Science (IFToMM)

Research Interests πŸ”¬

  • Tribology and lubrication in engine components

  • Scuffing behavior and wear modeling of piston components

  • Multi-phase and multi-scale engine wear modeling using quantum chemical molecular dynamics and machine learning

  • Digital twin modeling for tribocorrosion

  • Application of artificial intelligence and big data in engine tribology

  • Mechano-chemical kinetic models for boundary lubrication

Awards and HonorsπŸ†βœ¨

  • Technical committee member of IFToMM (Engines and Powertrains)

  • Contributor to Springer’s Mechanisms and Machine Science Series

  • Numerous high-impact journal publications in Tribology Transactions, ASME Journal of Tribology, Tribology International, and Wear

Conclusion🌟

Professor Chao Zhang is an accomplished mechanical engineering expert with a focus on tribology, engine wear, and computational modeling. His interdisciplinary research integrates classical tribology with advanced computational methods, positioning him as a leading figure in his field. His contributions to academia, industry collaborations, and publications underscore his commitment to advancing mechanical engineering and tribology.

Publications πŸ“š

1️⃣ Zhang, C. (2025). Multi-phase and multi-scale engine wear modeling via quantum chemical molecular dynamics and machine learning: A theoretical framework. πŸ”¬πŸ› οΈ Wear, xxx(xxx)xxx. [πŸ”— DOI: 10.1016/j.wear.2025.205771]


2️⃣ Zhang, C. (2023). Lubricant-Chemistry Kinetic Model of Antiwear Film Formation by Oil Additives using SOL, QM MD, and machine learning. πŸ”πŸ“Š STLE 2023 Annual Meeting Digital Proceedings.


3️⃣ Zhang, C. (2022). Scuffing behavior of piston-pin bore bearing in mixed lubrication. βš™οΈπŸ“– In T. Parikyan (Ed.), Advances in Engine and Powertrain Research and Technology (pp. 65–95). Springer, Mechanisms and Machine Science 114.


4️⃣ Zhang, C. (2022). Quantum chemical study of mechanochemical reactive mechanisms of engine oil antiwear additives. πŸ§ͺβš›οΈ Proceedings of I4SDG Workshop 2021, MMS 108, pp. 1–9.


5️⃣ Zhang, C. (2021). Scuffing factor and scuffing failure mapping. πŸš—πŸ”₯ Proceedings of the 2nd World Congress on Internal Combustion Engine, April 21-24, Jinan, China.


6️⃣ Zhang, C. (2018). Analysis of piston scuffing failure based on big data base and cloud computing. β˜οΈπŸ’Ύ Proceedings of the 2018 World Internal Combustion Engine Congress and Exhibition, November 8-11, Wuxi, China.


7️⃣ Zhang, C., et al. (2007). Effect of loading path on sliding contact status for elastic and plastic media. πŸ”©βš™οΈ Proceedings of the STLE/ASME International Joint Tribology Conference, IJTC2007-44481.


8️⃣ Ye, Z.K., Zhang, C., Wang, Y.C., Cheng, H.S, Tung, S. M., Wang, Q., He, J. (2004). An experimental investigation of piston skirt scuffing: a piston scuffing apparatus, experiments, and scuffing mechanism analyses. πŸ”πŸ”¬ WEAR, 257, 8-31.


9️⃣ Zhang, C., Wang, Q., Cheng, H. S. (2004). Scuffing Behavior of Piston-Pin/Bore Bearing in Mixed Lubrication – Part II: Scuffing Mechanism and Failure Criteria. πŸ› οΈβš‘ STLE, Tribology Transactions, 47, 149-156.


πŸ”Ÿ Zhang, C., Cheng, H. S., Qiu, L., Knipstein, K. W., & Bolyard, J. (2003). Scuffing Behavior of Piston-Pin/Bore Bearing in Mixed Lubrication – Part I: Experimental Studies. πŸ§‘β€πŸ”¬πŸ“Š STLE, Tribology Transactions, 46, 193-199.