Books:
Referred Chapters:
- 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:
2023
- Xiao, M., and Z. Wu, “Modeling and Control of a Chemical Process Network Using Physics-Informed Transfer Learning“, submitted.
- Zheng, Y., C. Hu, X, Wang, and Z. Wu, “Physics-Informed Recurrent Neural Network Modeling for Predictive Control of Nonlinear Processes“, J. Proc. Contr., in press.
- 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.
- Parker, S., Z. Wu and P. D. Christofides, “Cybersecurity in Process Control, Operations, and Supply Chain,” Comp. & Chem. Eng., 171, 108169, 2023.
- Xiao, M., C. Hu, and Z. Wu, “Modeling and Predictive Control of Nonlinear Processes Using Transfer Learning Method“, AIChE J., e18076, 2023.
- 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.
- 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.
- 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.
- 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.
- 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.
2022
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Chen, S., Z. Wu and P. D. Christofides, “A Cyber-secure Control-Detector Architecture for Nonlinear Processes,” AIChE J., 66, e16907, 2020.
- 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
- 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.
- 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.
- 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.
- 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)
- 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)
- 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.
- 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).
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- Wu, Z., H. Durand and P. D. Christofides, “Safe Economic Model Predictive Control of Nonlinear Systems,” Syst. & Contr. Lett., 118, 69-76, 2018.
- 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.
- 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
- 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
- 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
2023
- 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, in press , Yokohama, Japan, 2023.
- Hu, C. and Z. Wu, “Online-Learning-Based Economic MPC of Switched Nonlinear Systems,” Proceedings of 22nd International Federation of Automatic Control World Congress, in press , Yokohama, Japan, 2023.
- Hu, C. and Z. Wu, “Online Learning-Based Predictive Control of Switched Nonlinear Systems With Disturbances,” Proceedings of the American Control Conference, in press , San Diego, California, 2023.
- Xiao, M., C. Hu and Z. Wu, “Transfer Learning-Based Modeling and Predictive Control of Nonlinear Processes ,” Proceedings of the American Control Conference, in press , San Diego, California, 2023.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
- 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
- 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)
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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).
- 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.
- 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.