师资队伍

2011年8月-2016年7月 清华大学自动化系控制科学与工程专业学习，获工学博士学位

2007年8月-2011年7月 在清华大学自动化系专业学习，获工学学士学位

IEEE Member

中国自动化学会大数据专委会 委员

中国化工学会信息技术专业委员会 青年委员

IFAC期刊Control Engineering Practice青年编委

《控制工程》客座编委

IEEE TAC、Automatica等60多个SCI期刊审稿人

[1] 复杂工业过程建模、控制与优化

[2] 工业大数据解析理论方法与应用

[3] 不确定规划理论方法与应用

近年来，大数据技术的蓬勃发展为过程控制带来了新的机遇；然而，过程建模控制仍然严重依赖人的知识型工作，难以实现工业过程的长周期自主运行。课题组瞄准炼油、石化、光伏等我国国民经济发展重点行业核心技术难题，在工业大数据与智能制造方向开展了深入的研究工作，学术成果在多个行业中得到推广应用并取得了显著成效。近年来出版Springer英文专著1部，在IEEE Trans. Automatic Control、Automatica等期刊累计发表SCI论文近40篇，授权国家发明专利5项。

1. 国家“万人计划”青年拔尖人才，2021

2. Emerging Leaders of Control Engineering Practice, 2021

3. 清华大学年度教学优秀奖，2021

4. 张钟俊院士优秀论文奖，2020

5. Top Reviewer for Control Engineering Practice, 2020

6. Best Paper Award, International Conference on Industrial Artificial Intelligence, 2019

7. Springer Excellent Doctorate Theses Award, 2018

8. 清华大学教学成果奖一等奖，2016

9. 清华大学“紫荆学者”，2016

10.清华大学优秀博士论文一等奖，2016

学术专著

C. Shang (2018). Dynamic Modeling of Complex Industrial Processes: Data-Driven Methods and Application Research. Springer, 2018. ISBN 978-981-10-6676-4. (143 pages)

主要论文

[J33] Shang, C., Ding, Steven X., Ye, H., & Huang, D. (2022) From generalized Gauss bounds to distributionally robust fault detection with unimodality information. IEEE Transactions on Automatic Control. doi: 10.1109/TAC.2022.3220180. (Regular Paper)

[J32] Huo, K., Huang, D., & Shang, C. (2023) A novel white component analysis for dynamic process monitoring. Journal of Process Control, 127, 102998.

[J31] Sader, M., Li, W., Liu, Z., Jiang, H., & Shang, C. (2023). Semi-global fault-tolerant cooperative output regulation of heterogeneous multi-agent systems with actuator saturation. Information Sciences. 641, 119028.

[J30] Liu, Q., Shang, C., Liu, T., & Huang, D. (2023) Efficient relay autotuner of industrial controllers via rank-constrained identification of low-order time-delay models. IEEE Transactions on Control Systems Technology. 31(4), 1787-1802. (Regular Paper)

[J29] Li, K., Shang, C., & Ye, H. (2022) Reweighted regularized prototypical network for few-shot fault diagnosis. IEEE Transactions on Neural Networks and Learning Systems. doi: 10.1109/TNNLS.2022.3232394.

[J28] Shang, C., Wang, C., You, K., & Huang, D. (2022). Distributionally robust chance constraint with unimodality-skewness information and conic reformulation. Operations Research Letters, 50(2), 176-183.

[J27] Shang, C., & You, F. (2021). A posteriori probabilistic bounds of convex scenario programs with validation tests. IEEE Transactions on Automatic Control. 66(9), 4015-4028. (Regular Paper)

[J26] Shang, C., Ding, S. X., & Ye, H. (2021). Distributionally robust fault detection design and assessment for dynamical systems. Automatica. 125, 109434. (Regular Paper)

[J25] Shang, C., Huang, X., Ye, H., & Huang, D. (2021) Group-sparsity-enforcing fault discrimination and estimation with dynamic process data. Journal of Process Control, 105, 236-249.

[J24] Guo, Z., Shang, C., & Ye, H. (2021) A novel similarity metric with application to big process data analytics. Control Engineering Practice. 104843.

[J23] Han, B., Shang, C., & Huang, D. (2021) Multiple kernel learning-aided robust optimization: Learning procedure, computational tractability, and usage in multi-stage decision-making. European Journal of Operational Research. 292(3), 1004-1018.

[J22] Scott, D., Shang, C., Huang, B., & Huang, D. (2021). A holistic probabilistic framework for monitoring non-stationary dynamic industrial processes. IEEE Transactions on Control Systems Technology. 29(5), 2239-2246.

[J21] Liu, Q., Shang, C., & Huang, D. (2021). Efficient low-order system identification from low-quality step response data with rank-constrained optimization. Control Engineering Practice. 107, 104671. (Featured Paper)

[J20] Shang, C., Chen, W. H., Stroock, A. D., & You, F. (2020). Robust model predictive control of irrigation systems with active uncertainty learning and data analytics. IEEE Transactions on Control Systems Technology, 28, 1493-1504. (Regular Paper)

[J19] Shang, C., Ji, H., Huang, X., Yang, F., & Huang, D. (2019). Generalized grouped contributions for hierarchical fault diagnosis with group Lasso. Control Engineering Practice, 93, 104193.

[J18] Shang, C., & You, F. (2019). Data analytics and machine learning for smart process manufacturing: Recent advances and perspectives in the big data era. Engineering, 5(6), 1010-1016. (Invited Paper)

[J17] Shang, C., & You, F. (2019). A data-driven robust optimization approach to scenario-based stochastic model predictive control. Journal of Process Control, 75, 24-39.

[J16] Shang, C., & You, F. (2018). Distributionally robust optimization for planning and scheduling under uncertainty. Computers & Chemical Engineering, 110, 53-68.

[J15] Shang, C., Yang, F., Huang, B., & Huang, D. (2018). Recursive slow feature analysis for adaptive monitoring of industrial processes. IEEE Transactions on Industrial Electronics, 65(11), 8895-8905.

[J14] Li, F., Zhang, J., Shang, C., Huang, D., Oko, E., & Wang, M. (2018). Modelling of a post-combustion CO2 capture process using deep belief network. Applied Thermal Engineering, 130, 997-1003

[J13] Shang, C., Huang, X., & You, F. (2017). Data-driven robust optimization based on kernel learning. Computers & Chemical Engineering, 106, 464-479.

[J12] Gao, X., Shang, C., Huang, D., & Yang, F. (2017). A novel approach to monitoring and maintenance of industrial PID controllers. Control Engineering Practice, 64, 111-126.

[J11] Gao, X., Zhang, J., Yang, F., Shang, C., & Huang, D. (2017). Robust proportional–integral-derivative (PID) design for parameter uncertain second-order plus time delay (SOPTD) processes based on reference model approximation. Industrial & Engineering Chemistry Research, 56(41), 11903-11918.

[J10] Gao, X., Yang, F., Shang, C., & Huang, D. (2017). A novel data-driven method for simultaneous performance assessment and retuning of PID controllers. Industrial & Engineering Chemistry Research, 56(8), 2127-2139.

[J9] Shang, C., Huang, B., Yang, F., & Huang, D. (2016). Slow feature analysis for monitoring and diagnosis of control performance. Journal of Process Control, 39, 21-34.

[J8] Guo, F., Shang, C., Huang, B., Wang, K., Yang, F., & Huang, D. (2016). Monitoring of operating point and process dynamics via probabilistic slow feature analysis. Chemometrics and Intelligent Laboratory Systems, 151, 115-125.

[J7] Gao, X., Yang, F., Shang, C., & Huang, D. (2016). A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era. Chinese Journal of Chemical Engineering, 24(8), 952-962.

[J6] Shang, C., Huang, B., Yang, F., & Huang, D. (2015). Probabilistic slow feature analysis-based representation learning from massive process data for soft sensor modeling. AIChE Journal, 2015, 61(12), 4126-4139.

[J5] Shang, C., Yang, F., Gao, X., Huang, X., Suykens, J. A. K., & Huang, D. (2015). Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis. AIChE Journal, 2015, 61(11), 3666-3682.

[J4] Shang, C., Huang, X., Suykens, J. A. K., & Huang, D. (2015) Enhancing dynamic soft sensors based on DPLS: a temporal smoothness regularization approach. Journal of Process Control, 28, 17-26.

[J3] Gao, X., Shang, C., Jiang, Y., Huang, D., & Chen, T. (2014). Refinery scheduling with varying crude: A deep belief network classification and multimodel approach. AIChE Journal, 60(7), 2525-2532.

[J2] Shang, C., Yang, F., Huang, D., & Lyu, W. (2014). Data-driven soft sensor development based on deep learning technique. Journal of Process Control, 24(3), 223-233.

[J1] Shang, C., Gao, X., Yang, F., & Huang, D. (2014). Novel Bayesian framework for dynamic soft sensor based on support vector machine with finite impulse response. IEEE Transactions on Control Systems Technology, 22(4), 1550-1557.

目前指导博士生5名、本科生若干名；联合指导硕士生2名。

课题组长期招收系统辨识、过程控制、故障诊断、运筹优化等方向博士后，推荐申请博士后创新人才支持计划、博士后引进项目、清华大学“水木学者”支持计划等各类支持计划，欢迎来信咨询。