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

C. Shang, X. Yin, & T. Xue (2026). Toward distributional robustness in control: An emerging data-driven paradigm. In Encyclopedia of Systems and Control Engineering, vol. 3, 357-369. US: Elsevier.

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)

2.主要论文

[J46] Liu, Q., Wang, Y., Liu, T., Li, Z., He, X., & Shang, C. (2026) A novel state-space model identification method from a behavioral system-theoretic perspective. Journal of Process Control. 163, 103732.

[J45] Feng, Y., Jin, H., Ye, H., Ding, S. X., & Shang, C. (2026) Distributionally robust fault detection trade-off design with prior fault information. Control Engineering Practice, 169, 106758.

[J44] Qu, S., Dong, F., Wei, Z., & Shang, C. (2026) Towards an unsupervised learning scheme for efficiently solving parameterized mixed-integer programs. Computers & Operations Research, 185, 107290.

[J43] Xu, Y., Xue, W., Shang, C., & Fang, H. (2025) On globalized robust Kalman filter under model uncertainty. IEEE Transactions on Automatic Control. 70(2), 1147-1160.

[J42] Wang, Y., You, K., Huang, D., & Shang, C. (2025) Data-driven output prediction and control of stochastic systems: An innovation-based approach. Automatica. 171, 111897.

[J41] Sader, M., Wang, Y., Huang, D., Shang, C., & Huang, B. (2025) Causality-informed data-driven predictive control. IEEE Transactions on Control Systems Technology. 33(5), 1921-1928.

[J40] Huo, K., Huang, D., Yang, F., & Shang, C. (2025) Dynamic-inner white component analysis: A new latent variable model for dynamic data analytics and process monitoring. IEEE Transactions on Automation Science and Engineering. 22, 5614-5626.

[J39] Liu, J., Zhao, J., Ye, H., Huang, D., & Shang, C. (2025) A novel accuracy-constrained scheme for efficient trend extraction of industrial time-series data. IEEE Transactions on Instrumentation and Measurement. 74, 6503012.

[J38] Xue, T., Shang, C., Huang, D., & Huang, B. (2025) StictionGPT: Detecting valve stiction in control loops using large vision language model. Control Engineering Practice, 165, 106588.

[J37] Liu, Q., Liu, T., Huang, D., & Shang, C. (2025) Subspace identification of dynamic processes with consideration of time delays: A Bayesian optimization scheme. Journal of Process Control. 147, 103387.

[J36] Li, X., Dong, F., Wei, Z., & Shang, C. (2025) Data-driven contextual robust optimization based on support vector clustering. Computers & Chemical Engineering. 195, 109004.

[J35] He, Q., Liu, Q., Liang, Y., Lyu, W., Huang, D., & Shang, C. (2025) Risk-averse PID tuning based on scenario programming and parallel Bayesian optimization. Industrial & Engineering Chemistry Research. 64(1), 564-574.

[J34] Li, K., Shang, C., & Ye, H. (2024) Reweighted regularized prototypical network for few-shot fault diagnosis. IEEE Transactions on Neural Networks and Learning Systems. 35(5), 6206-6217.

[J33] Wang, Y., Qiu, Y., Sader, M., Huang, D., & Shang, C. (2023) Data-driven predictive control using closed-loop data: An instrumental variable approach. IEEE Control Systems Letters. 7, 3639-3644.

[J32] 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. 68(9), 5333-5348. (Regular Paper)

[J31] Huo, K., Huang, D., & Shang, C. (2023) A novel white component analysis for dynamic process monitoring. Journal of Process Control, 127, 102998.

[J30] 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.

[J29] 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)

[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. (ESI Highly Cited Article)

[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.