Publications

Pdf Access

PhD Student Dissertation

Presentations

Archive

 

Books:

  1. Wu, Z. and P. D. Christofides, “Process Operational Safety and Cybersecurity: A Feedback Control Approach,” Advances in Industrial Control Series, 277 pages, Springer, London, England, 2021.

Referred Chapters:

  1. Wang, Y., Y. Kadakia, Z. Wu, and P. D. Christofides, ”An Overview of Control Methods for Process Operational Safety and Cybersecurity,” Methods in Chemical Process Safety, Khan, F., E. N. Pistikopoulos and Z. Sajid (Eds.), 50 pages, Elsevier, Netherlands, 2025.
  2. Wang, Y., and Z. Wu, ”Machine learning in optimal control and process modeling,” High-Performance Computing and Artificial Intelligence in Process Engineering, Li, M., and Y. Heng (Eds.), in press, IOP Publishing, United Kingdom, 2025.
  3. Wu, Z., and P. D. Christofides, ”Smart Manufacturing: Machine Learning-Based Economic MPC and Preventive Maintenance,” Smart Manufacturing, M. Soroush, M. Baldea and T. F. Edgar (Eds.), Chapter 14, 21 pages, Elsevier, Netherlands, 2020.

Journal Articles:

2025

  1. Wu, G., W. Wu, Y. Wang, and Z. Wu, “Physics-Informed Graph Convolutional Recurrent Network for Cyber-Attack Detection in Chemical Process Networks“, Ind. & Eng. Chem. Res., in press.
  2. Zhang, H., Z, Wu, Q. Yuan, L. Guo, X. Li, C. Hua, P. Lu, “Control of Extractive Dividing Wall Column Using Model Predictive Control based on Long Short-Term Memory Networks“, Sep. Purif. Technol., 361, 131351, 2025.
  3. Xiao, M., H. Zhang, K. Vellayappan, K. Gudena, and Z. Wu, “A Modular Transfer Learning Approach for Complex Chemical Process Network Modeling“, Chem. Eng. Sci., 305, 121087, 2025.
  4. Wang, Y., and Z. Wu. “Machine Learning Model-Based Optimal Tracking Control of Nonlinear Affine Systems with Safety Constraints“, Inter. J. Rob. & Non. Contr., 35, 511-535, 2025.
  5. Wang, Z., D. Yu, and Z. Wu. “Real-Time Machine-Learning-Based Optimization Using Input Convex Long Short-Term Memory Network“, Appl. Energy, 377, 124472, 2025.
  6. Wu, Z., P. D. Christofides, W. Wu, Y. Wang, F. Abdullah, A. Alnajdi, Y. Kadakia, “A Tutorial Review of Machine Learning-Based Model Predictive Control Methods,”  Reviews in Chemical Engineering, 41, 83 pages, 2025.

 

2024

  1. Wang, Y., Ming, X., and Z. Wu. “Safe transfer reinforcement learning-based optimal control of nonlinear systems”, IEEE Trans. Cybern.,54, 7272-7284, 2024.
  2. Wang, W., H. Zhang, Y. Wang, Y. Tian, and Z. Wu. “Fast Explicit Machine Learning-Based Model Predictive Control Using Input Convex Neural Networks“,  Ind. & Eng. Chem. Res., 63, 17279-17293, 2024.
  3. Wang, Y., Z. Wu, and D. Ni. “Large-Scale Optimization Among Photovoltaic and Concentrated Solar Power Systems: A State-of-the-art Review and Algorithms Analysis“, Energies, 17, 4323, 2024.
  4. Xu, Z., Z. Yang, D. Wang, and Z. Wu, “Category-Guided Graph Convolution Network for Semantic Segmentation“, IEEE Trans. Netw. Sci. Eng., 11, 6080-6089, 2024.
  5. Hu, C., and Z. Wu. “Model Predictive Control of Switched Nonlinear Systems Using Online Machine Learning“, Chem. Eng. Res. & Des., 209, 221-236, 2024.
  6. Wang, Y., and Z. Wu. “Physics-Informed Reinforcement Learning for Optimal Control of Nonlinear Systems“, AIChE J., 70, e18542, 2024.
  7. Xu, Z., and Z. Wu. “Privacy Preserving Federated Machine Learning Modeling and Predictive Control of Heterogeneous Nonlinear Systems“, Comp. & Chem. Eng., 187, 108749, 2024.
  8. Wu, Z., C. Lee, and E. Ventura-Medina, “Integrating Cybersecurity into the Chemical Engineering Curriculum“, Feature Series Digitalisation In Education, The Chemical Engineer (IChemE), page 46, April 2024.
  9. Wang, W., Y. Wang, Y. Tian, and Z. Wu. “Explicit Machine Learning-Based Model Predictive Control of Nonlinear Processes via Multi-Parametric Programming“, Comp. & Chem. Eng., 186, 108689, 2024.
  10. Xiao, M., K. Vellayappan, PS Pravin, K. Gudena, and Z. Wu. “Optimization-Based Multi-Source Transfer Learning for Modeling of Nonlinear Processes“, Chem. Eng. Sci., 295, 120117, 2024.
  11. Wu, G., Y. Wang, and Z. Wu, “Physics-Informed Machine Learning in Cyber-Attack Detection and Resilient Control of Chemical Processes“, Chem. Eng. Res. & Des, 204, 544-555, 2024.
  12. Tan, W., M. Xiao, and Z. Wu. “Robust Reduced-Order Machine Learning Modeling of High-Dimensional Nonlinear Processes Using Noisy Data“, Dig. Chem. Eng., 11, 100145, 2024.
  13. Yang, X., Y. Ni, Z. Wu, W. Yang, and F. Liu. “Optimal Denial-of-Service Attack Scheduling for Remote State Estimation With Time-Varying Interference Power“, Inter. J. Rob. & Non. Contr., 34(6), 1-13, 2024.
  14. Wang, Y., Z. Wu, and D. Ni “Real-time Optimization of Heliostat Field Aiming Strategy via an IUPSO Algorithm“, Appl. Sci. 14(1), 416, 2024.
  15. Wang, Y., and Z. Wu. “Control Lyapunov-Barrier Function-Based Safe Reinforcement Learning for Nonlinear Optimal Control“, AIChE J., 70, e18306, 2024.
  16. Tan, W., and Z. Wu. “Robust Machine Learning Modeling for Predictive Control Using Lipschitz-Constrained Neural Networks“, Comp. & Chem. Eng., 180, 108466, 2024.

 

2023

  1. Wang, Z., W. Tan, G. P. Rangaiah and Z. Wu. “Machine Learning aided Model Predictive Control with Multi-Objective Optimization and Multi-Criteria Decision Making“, Comp. & Chem. Eng., 179, 108414, 2023.
  2. Hu, C., and Z. Wu, “Machine Learning-Based Model Predictive Control of Hybrid Dynamical Systems“, AIChE J., 69, e18210, 2023. (Future Issues)
  3. Wang, C., C.Hu, Y. Zheng, H. Jin and Z. Wu, “Predictive control of reactor network model using machine learning for hydrogen-rich gas and biochar poly-generation by biomass waste gasification in supercritical waste“, Energy, 282, 128441, 2023.
  4. Xiao, M., and Z. Wu, “Modeling and Control of a Chemical Process Network Using Physics-Informed Transfer Learning“, Ind. & Eng. Chem. Res., 62, 17216–17227, 2023.
  5. Zheng, Y., C. Hu, X, Wang, and Z. Wu, “Physics-Informed Recurrent Neural Network Modeling for Predictive Control of Nonlinear Processes“, J. Proc. Contr., 128, 103005, 2023.
  6. Wu, G., W. Tan, K. Le, and Z. Wu, “Physics-Informed Machine Learning for MPC: Application to a Batch Crystallization Process“, Chem. Eng. Res. & Des., 192, 556-569, 2023. (Most Downloaded Papers 2023/24)
  7. Parker, S., Z. Wu and P. D. Christofides, “Cybersecurity in Process Control, Operations, and Supply Chain,” Comp. & Chem. Eng., 171, 108169, 2023.
  8. Xiao, M., C. Hu, and Z. Wu, “Modeling and Predictive Control of Nonlinear Processes Using Transfer Learning Method“, AIChE J., 69, e18076, 2023.
  9. Hu, C., S. Chen, and Z. Wu, “Economic Model Predictive Control of Nonlinear Systems Using Online Learning of Neural Networks“, Processes, 11(2), 342, 2023.
  10. Zheng, Y., and Z. Wu, “Physics-Informed Online Machine Learning and Predictive Control of Nonlinear Processes With Parameter Uncertainty“, Ind. & Eng. Chem. Res., 62, 6, 2804–2818, 2023.
  11. Zheng, Y., S. Li, R. Wan, Z. Wu, and Y. Zhang, “Distributed Model Predictive Control For Reconfigurable Systems Based on Lyapunov Analysis“, J. Proc. Contr., 123, 1-11, 2023.
  12. Zhao, T., Y. Zheng, and Z. Wu, “Feature Selection-Based Machine Learning Modeling for Distributed Model Predictive Control of Nonlinear Processes“, Comp. & Chem. Eng., 169, 108074, 2023.
  13. Hu, C., Y. Cao, and Z. Wu, “Online Machine Learning Modeling and Predictive Control of Nonlinear Systems With Scheduled Mode Transitions“, AIChE J., 69, e17882, 2023. (Top Downloaded Paper 2022-2023)

 

 

2022

  1. Ren, Y. M., M. Alhajeri, J. Luo, S. Chen, F. Abdullah, Z. Wu and P. D. Christofides, “A Tutorial Review of Neural Network Modeling Approaches for Model Predictive Control,”  Comp. & Chem. Eng., 165, 107956, 2022.
  2. Wang, Z., J. Li, G. P. Rangaiah and Z. Wu, “Machine Learning aided Multi-Objective Optimization and Multi-Criteria Decision Making: Framework and Two Applications in Chemical Engineering“, Comp. & Chem. Eng., 165, 107945, 2022.
  3. Pravin P S , J. Tan, K. S. Yap, and Z. Wu, “Hyperparameter optimization strategies for machine learning-based stochastic energy efficient scheduling in cyber-physical production systems,” Digit. Chem. Eng., 4, 100047, 2022.
  4. Alhajeri, M., F. Albalawi, Z. Wu and P. D. Christofides, “Physics-informed Machine Learning Modeling for Predictive Control Using Noisy Data,” Chem. Eng. Res. & Des., 186, 34-49, 2022.
  5. Chen, S., Z. Wu and P. D. Christofides, “Statistical Machine-Learning-based Predictive Control Using Barrier Functions for Process Operational Safety,” Comp. & Chem. Eng., 163, 107860, 2022.
  6. Zheng, Y., T. Zhao, X. Wang, and Z. Wu, “Online Learning-Based Predictive Control of Crystallization Processes under Batch-to-Batch Parametric Drift,” AIChE J., 68, e17815, 2022.
  7. Zhang, H., P. Lu, Z. Ding, Y. Li, H. Li, C. Hua, and Z. Wu, “Design Optimization and Control of Dividing Wall Column for Purification of Trichlorosilane,” Chem. Eng. Sci., 257, 117716, 2022.
  8. Zhao, T., Y. Zheng, and Z. Wu, “Improving Computational Efficiency of Machine Learning Modeling of Nonlinear Processes Using Sensitivity Analysis and Active Learning ,” Digit. Chem. Eng., 3, 100027, 2022.
  9. Zheng, Y., X. Wang, and Z. Wu, “Machine Learning Modeling and Predictive Control of Batch Crystallization Process,” Ind. & Eng. Chem. Res., 61, 5578–5592, 2022.
  10. Zhao, T., Y. Zheng, J. Gong, and Z. Wu, “Machine Learning-Based Reduced-Order Modeling and Predictive Control of Nonlinear Processes,” Chem. Eng. Res. & Des., 179, 435-451, 2022.
  11. Wu, Z., A. Alnajdi, Q. Gu and P. D. Christofides, “Statistical Machine-Learning-based Predictive Control of Uncertain Nonlinear Processes,” AIChE J., 68, e17642, 2022.
  12. Chen, S., Z. Wu and P. D. Christofides, “Barrier-Function-Based Distributed Predictive Control for Operational Safety of Nonlinear Processes,” Comp. & Chem. Eng., 159, 107690, 2022.
  13. Alhajeri, M., J. Luo, Z. Wu, F. Albalawi and P. D. Christofides, “Process structure-based recurrent neural network modeling for predictive control: A comparative study,” Chem. Eng. Res. & Des., 179, 77-89, 2022.
  14. Luo, J., V. Canuso, J. B. Jang, Z. Wu, C. Morales-Guio and P. D. Christofides, “Machine Learning-Based Operational Modeling of an Electrochemical Reactor: Handling Data Variability and Improving Empirical Models,” Ind. & Eng. Chem. Res., 61, 8399-8410, 2022.
  15. Chen, S., Z. Wu and P. D. Christofides,  “Machine-Learning-Based Construction of Barrier Functions and Models for Safe Model Predictive Control,” AIChE J., 68, e17456, 2022.
  16. Abdoullah, F., Z. Wu and P. D. Christofides, “Handling noisy data in sparse model identification using subsampling and co-teaching,” Comp. & Chem. Eng., 157, 107628, 2022.
  17. Xiao, T., Z. Wu, P. D. Christofides, A. Armaou and D. Ni, “Recurrent neural network based model predictive control of a plasma etch process,” Ind. & Eng. Chem. Res., 61,  638-652, 2022.

 

 

2021

  1. Wu, Z., D. Rincon, Q. Gu and P. D. Christofides, “Statistical Machine Learning in Model Predictive Control of Nonlinear Processes,”  Mathematics, 9, 1912, 2021 (Best Paper Award, 2021).
  2. Dodhia, A., Z. Wu and P. D. Christofides, “Machine Learning-Based Predictive Control of Diffusion-Reaction Processes,” Chem. Eng. Res. & Des., 173, 129-139, 2021.
  3. Abdullah, F., Z. Wu and P. D. Christofides, “Sparse Identification-Based Model Predictive Control of Two-Time-Scale Processes,” Comp. & Chem. Eng., 153, 107411, 2021.
  4. Wu, Z., J. Luo, D. Rincon, and P. D. Christofides, “Machine Learning-based Predictive Control Using Noisy Data: Evaluating Performance and Robustness via a Large-Scale Process Simulator,” Chem. Eng. Res. & Des., 168, 275-287, 2021.
  5. Wu, Z., D. Rincon, J. Luo and P. D. Christofides, “Machine Learning Modeling and Predictive Control of Nonlinear Processes Using Noisy Data,” AIChE J., 67, e17164, 2021.
  6. Alhajeri, M., Z. Wu, F. Albalawi and P. D. Christofides, “Machine Learning-Based State Estimation and Predictive Control of Nonlinear Processes,” Chem. Eng. Res. & Des., 167, 268-280, 2021.
  7. Abdullah, F., Z. Wu and P. D. Christofides, “Data-Based Reduced-Order Modeling of Nonlinear Two-Time-Scale Processes,” Chem. Eng. Res. & Des., 166, 1-9, 2021.
  8. Chen, S., Z. Wu and P. D. Christofides, “Cyber-Security of Centralized, Decentralized, and Distributed Control-Detector Architectures for Nonlinear Processes,” Chem. Eng. Res. & Des., 165, 25-39, 2021.

 

2020

  1. Chen, S., Z. Wu, D. Rincon and P. D. Christofides, “Machine Learning-Based Distributed Model Predictive Control of Nonlinear Processes,” AIChE J., 66, e17013, 2020.
  2. Chen, S., Z. Wu and P. D. Christofides, “Decentralized Machine Learning-Based Predictive Control of Nonlinear Processes,” Chem. Eng. Res. & Des., 162, 45-60, 2020.
  3. Wang, Y., Y. Zhang, Z. Wu, H. Li and P. D. Christofides, “Operational Trend Prediction and Classification for Chemical Processes: A Novel Convolutional Neural Network Method Based on Symbolic Hierarchical Clustering,” Chem. Eng. Sci., 225, 115796, 2020.
  4.  Wu, Z., D. Rincon and P. D. Christofides, “Process Structure-based Recurrent Neural Network Modeling for Model Predictive Control of Nonlinear Processes,” J. Proc. Contr., 89, 74-84, 2020. (Most Cited Articles 2020-2023)
  5. Wu, Z., S. Chen, D. Rincon and P. D. Christofides, “Post Cyber-Attack State Reconstruction for Nonlinear Processes Using Machine Learning,” Chem. Eng. Res. &  Des., 159, 248-261, 2020.
  6. Chen, S., Z. Wu and P. D. Christofides, “Cyber-attack Detection and Resilient Operation of Nonlinear Processes under Economic Model Predictive Control,” Comp. & Chem. Eng., 136, 106806, 2020.
  7. Zhang, J., P. D. Christofides, X. He, Z. Wu, Y. Zhao and D. Zhou, “Robust Detection of Intermittent Sensor Faults in Stochastic LTV Systems,” Neurocomputing, 388, 181-187, 2020.
  8. Wu, Z., D. Rincon and P. D. Christofides, “Real-Time Adaptive Machine-Learning-Based Predictive Control of Nonlinear Processes,”  Ind. & Eng. Chem. Res., 59, 2275-2290, 2020.
  9. Wu, Z., D. Rincon and P. D. Christofides, “Real-time Machine Learning for Operational Safety of Nonlinear Processes via Barrier-Function Based Predictive Control,” Chem. Eng. Res. & Des., 155, 88-97, 2020.
  10. Chen, S., Z. Wu and P. D. Christofides, “A Cyber-secure Control-Detector Architecture for Nonlinear Processes,” AIChE J.,  66, e16907, 2020.
  11. Wu, Z. and P. D. Christofides, “Control Lyapunov-Barrier Function-Based Predictive Control of Nonlinear Processes Using Machine Learning Modeling,” Comp. & Chem. Eng., 134, 106706, 2020.

 

2019

  1. Wu, Z. and P. D. Christofides, “Optimizing Process Economics and Operational Safety via Economic MPC Using Barrier Functions and Recurrent Neural Network Models,” Chem. Eng. Res. & Des., 152, 455-465, 2019.
  2.  Zhang, Z., Z. Wu, D. Rincon and P. D. Christofides, “Real-Time Optimization and Control of Nonlinear Processes Using Machine Learning,” Mathematics, 7 (10), 890, 25 pages, 2019.
  3. Wu, Z.,  F. Albalawi, Z. Zhang, J. Zhang, H. Durand and P. D. Christofides, “Control Lyapunov-Barrier Function-Based Model Predictive Control of Nonlinear Systems,” Automatica,  109, 108508, 2019.
  4. Wu, Z., A. Tran, D. Rincon and P. D. Christofides, “Machine Learning-Based Predictive Control of Nonlinear Processes. Part II: Computational Implementation,” AIChE J.,  65, e16734, 2019. (Top Downloaded Paper 2018-2019)
  5. Wu, Z., A. Tran, D. Rincon and P. D. Christofides, “Machine Learning-Based Predictive Control of Nonlinear Processes. Part I: Theory,” AIChE J., 65, e16729, 2019. (Top Downloaded Paper 2018-2019)
  6.  Zhang, Z., Z. Wu, D. Rincon and P. D. Christofides, “Operation Safety via Model Predictive Control: The Torrance Refinery Accident Revisited,”  Chem. Eng. Res. & Des., 149, 138-146, 2019.
  7. Wu, Z. and P. D. Christofides, “Economic Machine-Learning-Based Predictive Control of Nonlinear Systems,” Mathematics, 7 (6), 494, 20 pages, 2019 (Best Paper Award, 2019).
  8. Zhang, Z., Z. Wu, D. Rincon and P. D. Christofides, “Operational Safety of an Ammonia Process Network via Model Predictive Control,” Chem. Eng. Res. & Des., 146, 277-289, 2019.
  9. Wu, Z., A. Tran, Y. M. Ren, C. S. Barnes, S. Chen and P. D. Christofides, “Model Predictive Control of Phthalic Anhydride Synthesis in a Fixed-Bed Catalytic Reactor via Machine Learning Modeling,” Chem. Eng. Res. & Des., 145, 173-183, 2019.
  10. Ding, Y., Y. Zhang, K. Kim, A. Tran, Z. Wu and P. D. Christofides, “Microscopic Modeling and Optimal Operation of Thermal Atomic Layer Deposition,” Chem. Eng. Res. & Des., 145, 159-172, 2019.
  11. Zhang, Z., Z. Wu, D. Rincon, C. Garcia and P. D. Christofides, “Operational Safety of Chemical Processes via Safeness-Index Based MPC: Two Large-Scale Case Studies,” Comp. & Chem. Eng., 125, 204-215, 2019.
  12. Wu, Z. and P. D. Christofides, “Handling Bounded and Unbounded Unsafe Sets in Control Lyapunov-Barrier Function-Based Model Predictive Control of Nonlinear Processes,” Chem. Eng. Res. & Des., 143, 140-149, 2019.

 

2018

  1. Zhang, J., P. D. Christofides, X. He, Z. Wu, Z. Zhang and D. Zhou, “Event-triggered Filtering and Intermittent Fault Detection for Time-varying Systems with Stochastic Parameter Uncertainty and Sensor Saturation,” Inter. J. Rob. & Non. Contr., 28, 4666-4680, 2018.
  2. Wu, Z., F . Albalawi, J. Zhang, Z. Zhang, H. Durand and P. D. Christofides, “Detecting and Handling Cyberattacks in Model Predictive Control of Chemical Processes,” Mathematics, 6 (10), 173, 22 pages, 2018.
  3. Wu, Z., J. Zhang, Z. Zhang, F. Albalawi, H. Durand, M. Mahmood, P. Mhaskar and P. D. Christofides, “Economic Model Predictive Control of Stochastic Nonlinear Systems,” AIChE J.,  64, 3312-3322, 2018.
  4. Wu, Z., H. Durand and P. D. Christofides, “Safe Economic Model Predictive Control of Nonlinear Systems,” Syst. & Contr. Lett., 118, 69-76, 2018.
  5. Wu, Z., H. Durand and P. D. Christofides, “Safeness Index-Based Economic Model Predictive Control of Stochastic Nonlinear Systems,”  Mathematics, 6 (5), 69, 19 pages, 2018.
  6. Zhang, Z., Z. Wu, H. Durand, F. Albalawi and P. D. Christofides, “On Integration of Feedback Control and Safety Systems: Analyzing Two Chemical Process Applications,” Chem. Eng. Res. & Des., 132, 616-626, 2018.

 

2017

  1. Wu, Z., A. Aguirre, A. Tran, H. Durand, D. Ni and P. D. Christofides, “Model Predictive Control of a Steam Methane Reforming Reactor Described by a CFD Model,”  Ind. & Eng. Chem. Res., 56, 6002-6011, 2017.

 

2016

  1. Lao, L., A. Aguirre, A. Tran, Z. Wu, H. Durand and P. D. Christofides, “CFD Modeling and Control of a Steam Methane Reforming Reactor,”  Chem. Eng. Sci., 148, 78-92, 2016.

 

Conference Proceedings

2025

  1. Xiao, M., H. Zhang, K. Vellayappan, and Z. Wu “Modular Learning for Modeling and Control of Chemical Process Networks“, Proceedings of the American Control Conference, in press, Denver, Colorado, 2025.
  2. Shi, Y., Y. Wang, and Z. Wu “Handling Output Constraints in Control of Nonlinear Systems Using Koopman-Operator-Based Robust Reference Governor“, Proceedings of the American Control Conference, in press, Denver, Colorado, 2025.
  3. Khodaverdian, A., G. Wu,  Z. Wu, P. D. Christofides, “Encrypted Machine Learning-Based Model Predictive Control of Nonlinear Systems“, Proceedings of the American Control Conference, in press, Denver, Colorado, 2025.

 

2024

  1. Wu, W., Y. Wang, H. Zhang, M. Chiu, and Z. Wu “Phased LSTM-Based MPC for Modeling and Control of Nonlinear Systems Using Asynchronous and Delayed Measurement Data“, Proceedings of the Conference on Decision and Control, in press, Milan, Italy, 2024.
  2. Wang, Y., Ming, X., and Z. Wu. “Fast Reinforcement Learning For Optimal Control of Nonlinear Systems Using Transfer Learning”, Proceedings of the Conference on Decision and Control, in press, Milan, Italy, 2024.
  3. Tan, W., M. Xing, G. Wu and  Z. Wu, “Machine Learning Modeling of Nonlinear Processes with Lyapunov Stability Guarantees” Proceedings of the American Control Conference, 528 – 535, Toronto, Canada, 2024.
  4. Wang, Y., and  Z. Wu, “Optimal Control of Nonlinear Systems With Input and State Constraints Using Koopman Operator” Proceedings of the American Control Conference, 4078 – 4083, Toronto, Canada, 2024.
  5. Xu, Z., and  Z. Wu, “Federated Learning-Based Distributed Model Predictive Control of Nonlinear Systems” Proceedings of the American Control Conference, 1256 – 1262, Toronto, Canada, 2024.

 

2023

  1. Wu, G. and  Z. Wu, “Machine Learning-Based MPC of Batch Crystallization Process Using Physics-Informed RNNs,” Proceedings of 22nd International Federation of Automatic Control World Congress, 56, 2, 2846-2851, Yokohama, Japan, 2023.
  2. Hu, C. and  Z. Wu, “Online-Learning-Based Economic MPC of Switched Nonlinear Systems,” Proceedings of 22nd International Federation of Automatic Control World Congress, 56, 2, 2840-2845, Yokohama, Japan, 2023.
  3. Hu, C. and  Z. Wu, “Online Learning-Based Predictive Control of Switched Nonlinear Systems With Disturbances,” Proceedings of the American Control Conference, 92-99, San Diego, California, 2023.
  4. Xiao, M., C. Hu and  Z. Wu, “Transfer Learning-Based Modeling and Predictive Control of Nonlinear Processes ,” Proceedings of the American Control Conference, 106-112, San Diego, California, 2023.
  5. Parker, S., Z. Wu and P. D. Christofides, “Cybersecurity in Process Control, Operations, and Supply Chain,” Proceedings of Foundations of Computer Aided Process Operations / Chemical Process Control, 20 pages, San Antonio, Texas, 2023.
  6. Zheng, Y. and  Z. Wu, “Physics-Informed Machine Learning Modeling for Model Predictive Control of Nonlinear Processes,” Proceedings of Foundations of Computer Aided Process Operations / Chemical Process Control, 6 pages, San Antonio, Texas, 2023.
  7. Hu, C. and  Z. Wu, “Online Machine Learning Modeling and Predictive Control of Switched Nonlinear Systems,” Proceedings of Foundations of Computer Aided Process Operations / Chemical Process Control, 6 pages, San Antonio, Texas, 2023.
  8. Abdullah, F., Z. Wu and P. D. Christofides, “Data-based Modeling and Control of Nonlinear Process Systems Using Sparse Identification: An Overview of Recent Results,” Proceedings of Foundations of Computer Aided Process Operations / Chemical Process Control, 6 pages, San Antonio, Texas, 2023.
  9. Alhajeri, M., A. Alnajdi, Z. Wu and P. D. Christofides, “Statistical Machine Learning in Model Predictive Control: An Overview of Recent Results,” Proceedings of Foundations of Computer Aided Process Operations / Chemical Process Control, 6 pages, San Antonio, Texas, 2023.

 

2022

  1. Zheng, Y. and Z. Wu, “Online Learning for Machine Learning-based Modeling and Predictive Control of Crystallization Processes under Batch-to-Batch Parametric Drift ,”  Proceedings of the 7th International Symposium on Advanced Control of Industrial Processes, 216-221, Vancouver, BC, Canada, 2022.
  2. Pravin P S , J. Tan, and Z. Wu, “Performance evaluation of various hyperparameter tuning strategies for uncertain parameter forecast using LSTM ,”  Proceedings of the 7th International Symposium on Advanced Control of Industrial Processes, 301-306, Vancouver, BC, Canada, 2022.
  3. Zheng, Y. and Z. Wu, “Predictive Control of Batch Crystallization Process Using Machine Learning,”  Proceedings of the 13th IFAC Symposium on Dynamics and Control of Process Systems, 798-803, Busan, Republic of Korea, 2022.
  4. Wu, Z., A. Alnajdi, Q. Gu and P. D. Christofides, “Machine-Learning-based Predictive Control of Nonlinear Processes with Uncertainty,”  Proceedings of the American Control Conference, 2810-2816, Atlanta, Georgia, 2022.
  5. Tan, J., K. S. Yap and Z. Wu, ”Analysis of Real-time Scheduling for Cyber-physical Production Systems”, Proceedings of the 12th Conference on Learning Factories, in press, Singapore, 2022.

 

 

2021

  1. Wu, Z., J. Luo, D. Rincon and P. D. Christofides, “Co-Teaching Approach to Machine Learning-based Predictive Control of Nonlinear Processes,”  Proceedings of 11th IFAC International Symposium on Advanced Control of Chemical Processes, 8 pages, Venice, Italy, 2021. (Keynote presentation)
  2. Alhajeri, M., Z. Wu, D. Rincon, F. Albalawi and P. D. Christofides, “Estimation-Based Predictive Control of Nonlinear Processes Using Recurrent Neural Networks,”   Proceedings of 11th IFAC International Symposium on Advanced Control of Chemical Processes, 6 pages, Venice, Italy, 2021.
  3. Chen, S., Z. Wu and P. D. Christofides,  “Cyber-Security of Decentralized and Distributed Control Architectures with Machine-Learning Detectors for Nonlinear Processes,” Proceedings of the American Control Conference, 3264-3271, New Orleans, Louisiana, 2021.
  4. Wu, Z., D. Rincon and P. D. Christofides, “Handling Noisy Data in Machine Learning Modeling and Predictive Control of Nonlinear Processes,”  Proceedings of the American Control Conference, 3336-3342, New Orleans, Louisiana, 2021.
  5.  Wu, Z., D. Rincon and P. D. Christofides, “Improving Machine Learning Modeling of Nonlinear Processes Under Noisy Data Via Co-teaching Method,” Proceedings of the American Control Conference, 4650-4656, New Orleans, Louisiana, 2021.

 

2020

  1. Chen, S., Z. Wu and P. D. Christofides, “Machine Learning-Based Cyber-attack Detection and Resilient Operation via Economic Model Predictive Control for Nonlinear Processes,”  Proceedings of the 28th Mediterranean Conference on Control and Automation, 794-801, Saint-Raphael, France, 2020.
  2. Ding, Y., Y. Zhang, Z. Wu and P. D. Christofides, “Run-to-Run Control of Thermal Atomic Layer Deposition,” Proceedings of the 28th Mediterranean Conference on Control and Automation, 1080-1086, Saint-Raphael, France, 2020.
  3. Wu, Z., D. Rincon, M. Park and P. D. Christofides, “Economic MPC of Nonlinear Processes via Recurrent Neural Networks Using Structural Process Knowledge,” Proceedings of 21st International Federation of Automatic Control World Congress, Paper VI161-09.11, 7 pages, Berlin, Germany, 2020.
  4. Wu, Z., D. Rincon and P. D. Christofides, “Real-time Machine Learning-Based CLBF-MPC of Nonlinear Systems,” Proceedings of 21st International Federation of Automatic Control World Congress, Paper VI161-09.8, 6 pages, Berlin, Germany, 2020.
  5. Wu, Z., D. Rincon and P. D. Christofides, “Incorporating Structural Process Knowledge in Recurrent Neural Network Modeling of Nonlinear Processes,” Proceedings of the American Control Conference, 2413-2418, Denver, Colorado, 2020.
  6. Wu, Z. and P. D. Christofides, “Control Lyapunov-Barrier Function-Based Predictive Control of Nonlinear Systems Using Machine Learning Models,”  Proceedings of American Control Conference, 2754-2759, Denver, Colorado, 2020.

 

2019

  1. Wu, Z., A. Tran, Y. M. Ren, C. S. Barnes and P. D. Christofides, “Computational Fluid Dynamics Modeling and Control of Phthalic Anhydride Synthesis in a Fixed-Bed Catalytic Reactor,” Proceedings of the American Control Conference, 2151-2157, Philadelphia, Pennsylvania, 2019.
  2. Wu, Z. and P. D. Christofides, “On Impact of Unsafe Set Structure in Control Lyapunov-Barrier Function-Based Model Predictive Control,” Proceedings of the American Control Conference, 989-994, Philadelphia, Pennsylvania, 2019.
  3. Zhang, Z., Z. Wu, D. Rincon and P. D. Christofides, “Integrating Safeness-Index Based MPC and Safety Relief Valve Activation for Operational Safety of Chemical Processes,” Proceedings of the American Control Conference, 995-1001, Philadelphia, Pennsylvania, 2019.
  4. Aiello, E. M., Z. Wu, P. D. Christofides, C. Toffanin and L. Magni, “Improving Diabetes Conventional Therapy via Machine Learning Modeling,” Proceedings of the American Control Conference, 4136-4143, Philadelphia, Pennsylvania, 2019.
  5. Wu, Z., A. Tran, Y. M. Ren, C. S. Barnes, S. Chen and P. D. Christofides, “Machine Learning-Based Model Predictive Control of Distributed Chemical Processes,” Proceedings of IFAC CPDE/CDPS-2019, 8 pages, Oaxaca, Mexico, 2019.

 

2018

  1. Wu, Z., H. Durand and P. D. Christofides, “Control Lyapunov-Barrier Function-Based Economic Model Predictive Control of Nonlinear Systems,” Proceedings of IFAC NMPC-2018, 48-53, Madison, Wisconsin, 2018.
  2. Wu, Z., H. Durand and P. D. Christofides, “Handling Process Safety and Stochastic Uncertainty in Economic Model Predictive Control,” Proceedings of IFAC NMPC-2018, 424-429, Madison, Wisconsin, 2018.
  3. Zhang, Z., Z. Wu, H. Durand, F. Albalawi and P. D. Christofides, “On Integration of Model Predictive Control with Safety System: Preventing Thermal Runaway,”  Proceedings of 13th International Symposium on Process Systems Engineering – PSE 2018, Computer-Aided Chemical Engineering, 44, 2011-2016, San Diego, California, 2018.
  4. Wu, Z., F. Albalawi, Z. Zhang, J. Zhang, H. Durand and P. D. Christofides, “Model Predictive Control for Process Operational Safety: Utilizing Safeness Index-Based Constraints and Control Lyapunov-Barrier Functions,” Proceedings of 13th International Symposium on Process Systems Engineering – PSE 2018, Computer-Aided Chemical Engineering, 44, 505-510, San Diego, California, 2018 (Keynote presentation).
  5. Wu, Z., F. Albalawi, Z. Zhang, J. Zhang, H. Durand and P. D. Christofides, “Control Lyapunov-Barrier Function-Based Model Predictive Control of Nonlinear Systems,” Proceedings of the American Control Conference, 5920-5926, Milwaukee, Wisconsin, 2018.
  6. Wu, Z., J. Zhang, Z. Zhang, F. Albalawi, H. Durand, M. Mahmood, P. Mhaskar and P. D. Christofides, “Lyapunov-Based Economic Model Predictive Control of Stochastic Nonlinear Systems,” Proceedings of the American Control Conference, 3900-3907, Milwaukee, Wisconsin, 2018.

 

 

 

 

Viewing Message: 1 of 1.
 Warning

Blog.nus accounts will move to SSO login soon. Once implemented, only current NUS staff and students will be able to log in to Blog.nus. Public blogs remain readable to non-logged in users. (More information.)