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 Profileย 

Scopus

Orcid

Educational Background ๐ŸŽ“

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

Professional Experience ๐Ÿ’ผ

  • 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 ๐Ÿ”ฌ

  • 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 Honors๐Ÿ†โœจ

  • 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

Conclusion๐ŸŒŸ

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 ๐Ÿ“š

๐Ÿ“˜ Data-driven second-order iterative sliding mode control for cyberโ€“physical systems under prescribed performance and DoS attacks
โœ๏ธ Yijie Yang, Dong Liu, Xin Wang, Zhujun Wang
๐Ÿ—ž๏ธ Journal of Process Control, June 2025
๐Ÿ”— DOI: 10.1016/j.jprocont.2025.103422


๐Ÿ“˜ Prescribed performance based data-driven adaptive sliding mode control for discrete-time nonlinear systems
โœ๏ธ Dong Liu, Yi-Jie Yang, Li-Ying Hao
๐Ÿ—ž๏ธ Journal of the Franklin Institute, March 2024
๐Ÿ”— DOI: 10.1016/j.jfranklin.2024.01.021


๐Ÿ“˜ Data-Driven Bipartite Consensus Tracking for Nonlinear Multiagent Systems With Prescribed Performance
โœ๏ธ Dong Liu, Zhi-Peng Zhou, Tie-Shan Li
๐Ÿ—ž๏ธ IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2023
๐Ÿ”— DOI: 10.1109/TSMC.2022.3230504


๐Ÿ“˜ Eventโ€triggered modelโ€free adaptive control for nonlinear systems with output saturation
โœ๏ธ Dong Liu, Ning Liu, Tieshan Li
๐Ÿ—ž๏ธ Int. Journal of Robust and Nonlinear Control, August 2023
๐Ÿ”— DOI: 10.1002/rnc.6747


๐Ÿ“˜ Data-Driven Adaptive Sliding Mode Control of Nonlinear Discrete-Time Systems With Prescribed Performance
โœ๏ธ Dong Liu, Guang-Hong Yang
๐Ÿ—ž๏ธ IEEE Trans. on Systems, Man, and Cybernetics: Systems, December 2019
๐Ÿ”— DOI: 10.1109/TSMC.2017.2779564


๐Ÿ“˜ Prescribed Performance Model-Free Adaptive Integral Sliding Mode Control for Discrete-Time Nonlinear Systems
โœ๏ธ Dong Liu, Guang-Hong Yang
๐Ÿ—ž๏ธ IEEE Trans. on Neural Networks and Learning Systems, July 2019
๐Ÿ”— DOI: 10.1109/TNNLS.2018.2881205


๐Ÿ“˜ Model-Free Adaptive Control Design for Nonlinear Discrete-time Processes with Reinforcement Learning Techniques
โœ๏ธ Dong Liu
๐Ÿ—ž๏ธ International Journal of Systems Science, July 2018
๐Ÿ”— [Link not provided]


๐Ÿ“˜ Performance-based data-driven model-free adaptive sliding mode control for a class of discrete-time nonlinear processes
โœ๏ธ Dong Liu
๐Ÿ—ž๏ธ Journal of Process Control, June 2018
๐Ÿ”— [Link not provided]


๐Ÿ“˜ Data-Driven Adaptive Sliding Mode Control of Nonlinear Discrete-Time Systems With Prescribed Performance
โœ๏ธ Dong Liu
๐Ÿ—ž๏ธ IEEE Trans. on Systems, Man, and Cybernetics: Systems, November 2017
๐Ÿ”— [Link not provided]


๐Ÿ“˜ Event-based model-free adaptive control for discrete-time non-linear processes
โœ๏ธ Dong Liu
๐Ÿ—ž๏ธ IET Control Theory & Applications, September 2017
๐Ÿ”— [Link not provided]


๐Ÿ“˜ Neural network-based event-triggered MFAC for nonlinear discrete-time processes
โœ๏ธ Dong Liu
๐Ÿ—ž๏ธ Neurocomputing, July 2017
๐Ÿ”— [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 Profileย 

Scopus

Educational Background ๐ŸŽ“

  • Bachelorโ€™s Degree: Mechanical Engineering, Shanghai Railway University (1983)

  • Masterโ€™s Degree: Mechanical Engineering, Shanghai Internal Combustion Engine Research Institute (1989)

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

Professional Experience ๐Ÿ’ผ

  • Senior Research Associate (1997โ€“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 ๐Ÿ”ฌ

  • 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 Honors๐Ÿ†โœจ

  • Technical committee member of IFToMM (Engines and Powertrains)

  • Contributor to Springerโ€™s Mechanisms and Machine Science Series

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

Conclusion๐ŸŒŸ

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 ๐Ÿ“š

1๏ธโƒฃ Zhang, C. (2025). Multi-phase and multi-scale engine wear modeling via quantum chemical molecular dynamics and machine learning: A theoretical framework. ๐Ÿ”ฌ๐Ÿ› ๏ธ Wear, xxx(xxx)xxx. [๐Ÿ”— DOI: 10.1016/j.wear.2025.205771]


2๏ธโƒฃ Zhang, C. (2023). Lubricant-Chemistry Kinetic Model of Antiwear Film Formation by Oil Additives using SOL, QM MD, and machine learning. ๐Ÿ”๐Ÿ“Š STLE 2023 Annual Meeting Digital Proceedings.


3๏ธโƒฃ Zhang, C. (2022). Scuffing behavior of piston-pin bore bearing in mixed lubrication. โš™๏ธ๐Ÿ“– In T. Parikyan (Ed.), Advances in Engine and Powertrain Research and Technology (pp. 65โ€“95). Springer, Mechanisms and Machine Science 114.


4๏ธโƒฃ Zhang, C. (2022). Quantum chemical study of mechanochemical reactive mechanisms of engine oil antiwear additives. ๐Ÿงชโš›๏ธ Proceedings of I4SDG Workshop 2021, MMS 108, pp. 1โ€“9.


5๏ธโƒฃ Zhang, C. (2021). Scuffing factor and scuffing failure mapping. ๐Ÿš—๐Ÿ”ฅ Proceedings of the 2nd World Congress on Internal Combustion Engine, April 21-24, Jinan, China.


6๏ธโƒฃ Zhang, C. (2018). Analysis of piston scuffing failure based on big data base and cloud computing. โ˜๏ธ๐Ÿ’พ Proceedings of the 2018 World Internal Combustion Engine Congress and Exhibition, November 8-11, Wuxi, China.


7๏ธโƒฃ Zhang, C., et al. (2007). Effect of loading path on sliding contact status for elastic and plastic media. ๐Ÿ”ฉโš™๏ธ Proceedings of the STLE/ASME International Joint Tribology Conference, IJTC2007-44481.


8๏ธโƒฃ 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. ๐Ÿ”๐Ÿ”ฌ WEAR, 257, 8-31.


9๏ธโƒฃ 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. ๐Ÿ› ๏ธโšก STLE, Tribology Transactions, 47, 149-156.


๐Ÿ”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. ๐Ÿง‘โ€๐Ÿ”ฌ๐Ÿ“Š STLE, Tribology Transactions, 46, 193-199.