尚超 - 清华大学 - 自动化系

个人简介

教育背景 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，获工学学士学位工作履历 2022.06-至今清华大学自动化系副教授 2018.10-2022.05 清华大学自动化系助理教授 2016.10-2018.10 美国康奈尔大学 & 清华大学博士后学术兼职 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期刊审稿人奖励与荣誉 Emerging Leaders of Control Engineering Practice, 2021 清华大学年度教学优秀奖，2021 张钟俊院士优秀论文奖，2020 Top Reviewer for Control Engineering Practice, 2020 Best Paper Award, International Conference on Industrial Artificial Intelligence, 2019 Springer Excellent Doctorate Theses Award, 2018 清华大学教学成果奖一等奖，2016 清华大学“紫荆学者”，2016 清华大学优秀博士论文一等奖，2016

研究领域

复杂工业过程建模、控制与优化工业大数据解析理论方法与应用不确定规划理论方法与应用

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

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 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) Huo, K., Huang, D., & Shang, C. (2023) A novel white component analysis for dynamic process monitoring. Journal of Process Control, 127, 102998. 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. 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) 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. 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. 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) Shang, C., Ding, S. X., & Ye, H. (2021). Distributionally robust fault detection design and assessment for dynamical systems. Automatica. 125, 109434. (Regular Paper) 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. Guo, Z., Shang, C., & Ye, H. (2021) A novel similarity metric with application to big process data analytics. Control Engineering Practice. 104843. 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. 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. 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) 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) 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. 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) 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. Shang, C., & You, F. (2018). Distributionally robust optimization for planning and scheduling under uncertainty. Computers & Chemical Engineering, 110, 53-68. 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. 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 Shang, C., Huang, X., & You, F. (2017). Data-driven robust optimization based on kernel learning. Computers & Chemical Engineering, 106, 464-479. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.