Introduction
Youqing Wang received the B.S. degree in Mathematics from Shandong University, Jinan, Shandong, China, in 2003, and Ph.D. degree in Control Science and Engineering from Tsinghua University, Beijing, China, in 2008. He worked chronologically at Hong Kong University of Science and Technology, Hong Kong, China; University of California, Santa Barbara, CA, USA; University of Alberta, Edmonton, AB, Canada; Shandong University of Science and Technology, Qingdao, Shandong, China; City University of Hong Kong, Hong Kong, China. He is currently a Professor at Beijing University of Chemical Technology, Beijing, China. His research interests include fault diagnosis, fault-tolerant control, state monitoring, and iterative learning control for chemical and biomedical processes. Dr. Wang was a recipient of several honors and awards, including IET Fellow, NSFC Distinguished Young Scientists Fund, Journal of Process Control Survey Paper Prize, and ADCHEM2015 Young Author Prize.
Research
In the field of fault diagnosis: Conducted systematic research on the fault estimation problem of two-dimensional dynamic systems, providing several necessary and sufficient conditions for the joint estimation of state and fault (asymptotical estimation, unbiased minimum variance estimation, H-infinity estimation). Designed various fault detection algorithms based on multivariate statistics, presented several sufficient conditions for fault detectability, and analyzed the computational complexity of the algorithms. In the field of fault-tolerant control: A robust fault-tolerant control strategy was first designed for a class of non-minimum phase nonlinear systems. Initiated internationally the research on fault-tolerant control for batch processes; some achievements have been practically applied in the injection molding processes, achieving notable results. In the field of artificial pancreas: Pioneered the application of iterative learning control to the closed-loop controller design for the artificial pancreas systems, introducing the concept of L-MPC, and conducted clinical trials. Proposed an automatic bolus and adaptive basal algorithm, realized full closed-loop control, and conducted clinical trials.
Teaching
Tailoring my approach to the distinct needs of undergraduates and graduate students, I have introduced two relevant courses: “Modern Control Theory” and “Linear System Theory.” For undergraduates, I comprehensively expound upon fundamental knowledge, nurturing their academic interests and foundational capabilities. For graduate students, the focus is on cutting-edge theories and practical applications to foster their independent research and innovation capabilities. Modern Control Theory Linear System Theory
Funding
001/2025-12/2029: Key Project of National Natural Science Foundation of China (Grant No. 62433004): Integrated analysis and monitoring of comprehensive safety in complex chemical processes Responsibility: Principal Investigator 01/2023-12/2027: Distinguished Youth Fund of National Natural Science Foundation of China (Grant No. 62225303): Safety assessment for intelligent chemical engineering systems Responsibility: Principal Investigator 01/2019-12/2021: Excellent Youth Fund of National Natural Science Foundation of China (Grant No. 61822308): Safety control of blood glucose dynamic processes Responsibility: Principal Investigator
Publications
Wang, Y.; Dong, Y.; Wang, H.; Xiao, M.; Zhu, S.; Liang, L.; Ma, X.* (2025): Spatiotemporal local analysis for nonlinear dynamic process monitoring. IEEE Transactions on Instrumentation & Measurement, 74, 3547312. DOI: 10.1109/TIM.2025.3580865 Yin, M.; Ma, X.; Wang, Y.* (2025): Semi-supervised multitask learning approach boosted by operation strategy expert system for industrial process fault diagnosis. IEEE Sensors Journal, DOI: 10.1109/JSEN.2025.3577708 Cui, M.; Wang, Y.; Guo, J.; Hou, T.; Ma, X.* (2025): A dynamic process modeling method based on bipartite graph and recursive monitoring for catalytic cracking unit. IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2025.3577537 Wang, Y.; Hou, W.; Liang, L.* (2025): Prescribed performance optimal fault-tolerant control for nonlinear systems with mismatched disturbances via zero-sum differential game. ISA Transactions, DOI: https://doi.org/10.1016/j.isatra.2025.05.026 Wang, Y.; Wang, H.; Hou, T.; Ye, X.; Simani, S.; Ma, X.* (2025): Jarque-Bera-based artificial neural correlation analysis for nonlinear and non-Gaussian process monitoring. IEEE Transactions on Systems, Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2025.3574056 Wang, Y.; Yin, T.; Ma, X.; Liang, L.; Guo, J.* (2025): FIGNN: fuzzy inference-guided graph neural network for fault diagnosis in industrial processes. IEEE Transactions on Instrumentation & Measurement, DOI: 10.1109/TIM.2025.3575985 He, W.; Shi, Y.; Wang. Y.* (2025): Adaptive prescribed-time control of switched nonlinear systems with false data injection attacks. IEEE Transactions on Circuits and Systems I: Regular Papers, DOI: 10.1109/TCSI.2025.3568861 Guo, J.; Sun, Y.; Ma, X.; Gao, J.; Hu, Y.; Wang, Y.*; Yin, B. (2025): Globality meets locality: an anchor graph collaborative learning framework for fast multi-view subspace clustering. IEEE Transactions on Neural Networks and Learning Systems, 36(6): 10213-10227. DOI: 10.1109/TNNLS.2025.3545435 Wang, Y.; Li, Y.; Parisini, T.; Zhao, D.* (2025): Resilient distributed control for uncertain nonlinear interconnected systems under replay cyber-attacks. IEEE Transactions on Automatic Control, 70(8): 5429-5443. DOI: 10.1109/TAC.2025.3546000 Zhao, J.; Shi, Y.; Wang, Y.* (2025): Self-healing control for multivariable processes based on simple and canonical correlation analyses. IEEE Transactions on Automation Science and Engineering, DOI: 10.1109/TASE.2025.3543328 Cui, M.; Ma, X.; Guo, J.; Hou, T.; Wang, Y.* (2025): Optimal sparse principal component analysis with a varying regularization coefficient for industrial fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 74: 3506413. DOI: 10.1109/TIM.2025.3527600 Cui, M.; Ma, X.; Wang, Y.*; Guo, J.; Hou, T. (2025): Fast sparse dynamic matrix estimation method with differential information for industrial process monitoring. IEEE Transactions on Control Systems Technology, 22(2): 512-525. DOI: 10.1109/TCST.2024.3483431 Wang, Z.; Ye, H.; Wang, Y.; Shi, Y.; Liang, L.* (2025): Optimal output–feedback controller design using adaptive dynamic programming: a permanent magnet synchronous motor application. IEEE Transactions on Circuits and Systems--II: Express Briefs, 72(1): 208-212. DOI: 10.1109/TCSII.2024.3483909 Shi, Y.; He, W.; Liang, L.; Wang, Y.* (2025): Distributed filter under homologous sensor attack and its application in GPS meaconing attack. IEEE Transactions on Automation Science and Engineering, 22: 5284-5292. DOI: 10.1109/TASE.2024.3418386
Awards
State Monitoring of Industrial Systems Based on Multivariate Statistics and Deep Learning, Natural Science First Prize, Chinese Association of Automation, 2023 IET Fellow, 2022 Fault Estimation and Fault-Tolerant Control of Two-Dimensional Systems, Natural Science Second Prize, Shandong Provincial People's Government, 2021
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