Pascal Vrignat | Industry 4.0 | Research Excellence Award

Published on by Innovation Technologist

Dr. Pascal Vrignat | Industry 4.0 | Research Excellence Award

Prisme Laboratory at Orleans University | France

Pascal Vrignat is a researcher specializing in operational safety, diagnostics, prognostics, and maintenance strategies for complex systems, with particular expertise in Markovian and stochastic models. His work significantly advances methods for estimating system degradation using survival laws, hidden Markov models, and Remaining Useful Life approaches. He contributes to understanding system obsolescence and managing shortages across the life cycle of industrial systems. His research bridges theory and industrial application, encompassing industrial computing, advanced process control, human–machine interfaces, SCADA systems, IoT, M2M technologies, and digital communication protocols, including OPC-based architectures. He has an extensive record of scientific output, including journal publications, conference papers, book chapters, and a widely used textbook on industrial local networks. His recent works address bearing degradation monitoring and the role of AI in sustainability-focused applications. He is active in research project development, editorial responsibilities, and academic leadership within his institution and research laboratory. His contributions to industry-oriented R&D have earned recognition in international automation competitions. His scholarly impact is reflected in 618 citations (405 since 2020), an h-index of 10 (7 since 2020), and an i10-index of 13 (6 since 2020), underscoring his sustained influence in the fields of reliability engineering, automation, predictive maintenance, and digital industrial systems.

Profiles: Orcid | Google Scholar

Featured Publications

Vrignat, P., Kratz, F., & Avila, M. (2022). Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review. Reliability Engineering & System Safety, 218, 108140. https://doi.org/10.1016/j.ress.2021.108140
Cited by: 152

Pascal, V., Toufik, A., Manuel, A., Florent, D., & Kratz, F. (2019). Improvement indicators for total productive maintenance policy. Control Engineering Practice, 82, 86–96. https://doi.org/10.1016/j.conengprac.2018.09.019
Cited by: 81

Vrignat, P., Avila, M., Duculty, F., & Kratz, F. (2015). Failure event prediction using hidden Markov model approaches. IEEE Transactions on Reliability, 64(3), 1038–1048. https://doi.org/10.1109/TR.2015.2426458
Cited by: 49

Aggab, T., Avila, M., Vrignat, P., & Kratz, F. (2021). Unifying model-based prognosis with learning-based time-series prediction methods: Application to Li-ion battery. IEEE Systems Journal, 15(4), 5245–5254. https://doi.org/10.1109/JSYST.2021.3080125
Cited by: 32

Vrignat, P., Avila, M., Duculty, F., Aupetit, S., Slimane, M., & Kratz, F. (2012). Maintenance policy: Degradation laws versus Hidden Markov Model availability indicator. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 226(2), 137–155. https://doi.org/10.1177/1748006X11406335
Cited by: 21

 

Dong Liu | Data Driven Control | Best Researcher Award

Published on by Innovation Technologist

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 Profile 

Scopus

Orcid

Educational Background 🎓

  • Ph.D. in Control Theory and Control Engineering,
    Northeastern University, China — 2018

Professional Experience 💼

  • 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 🔬

  • 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 Honors🏆✨

  • 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 📚

📘 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
🗞️ Journal of Process Control, June 2025
🔗 DOI: 10.1016/j.jprocont.2025.103422

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

📘 Data-Driven Bipartite Consensus Tracking for Nonlinear Multiagent Systems With Prescribed Performance
✍️ Dong Liu, Zhi-Peng Zhou, Tie-Shan Li
🗞️ IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2023
🔗 DOI: 10.1109/TSMC.2022.3230504

📘 Event‐triggered model‐free adaptive control for nonlinear systems with output saturation
✍️ Dong Liu, Ning Liu, Tieshan Li
🗞️ Int. Journal of Robust and Nonlinear Control, August 2023
🔗 DOI: 10.1002/rnc.6747

📘 Data-Driven Adaptive Sliding Mode Control of Nonlinear Discrete-Time Systems With Prescribed Performance
✍️ Dong Liu, Guang-Hong Yang
🗞️ IEEE Trans. on Systems, Man, and Cybernetics: Systems, December 2019
🔗 DOI: 10.1109/TSMC.2017.2779564

📘 Prescribed Performance Model-Free Adaptive Integral Sliding Mode Control for Discrete-Time Nonlinear Systems
✍️ Dong Liu, Guang-Hong Yang
🗞️ IEEE Trans. on Neural Networks and Learning Systems, July 2019
🔗 DOI: 10.1109/TNNLS.2018.2881205

📘 Model-Free Adaptive Control Design for Nonlinear Discrete-time Processes with Reinforcement Learning Techniques
✍️ Dong Liu
🗞️ International Journal of Systems Science, July 2018
🔗 [Link not provided]

📘 Performance-based data-driven model-free adaptive sliding mode control for a class of discrete-time nonlinear processes
✍️ Dong Liu
🗞️ Journal of Process Control, June 2018
🔗 [Link not provided]

📘 Data-Driven Adaptive Sliding Mode Control of Nonlinear Discrete-Time Systems With Prescribed Performance
✍️ Dong Liu
🗞️ IEEE Trans. on Systems, Man, and Cybernetics: Systems, November 2017
🔗 [Link not provided]

📘 Event-based model-free adaptive control for discrete-time non-linear processes
✍️ Dong Liu
🗞️ IET Control Theory & Applications, September 2017
🔗 [Link not provided]

📘 Neural network-based event-triggered MFAC for nonlinear discrete-time processes
✍️ Dong Liu
🗞️ Neurocomputing, July 2017
🔗 [Link not provided]