Wentao Tang

Tang, W., Carrette, P., Cai, Y., Williamson, J. M., & Daoutidis, P. (2023). Automatic decomposition of large-scale industrial processes for distributed MPC on the Shell-Yokogawa Platform for Advanced Control and Estimation (PACE). COMPUTERS & CHEMICAL ENGINEERING, 178. https://doi.org/10.1016/j.compchemeng.2023.108382

Tang, W. (2023, May 10). Data-Driven State Observation for Nonlinear Systems based on Online Learning. https://doi.org/10.22541/au.168368974.45377787/v1

Tang, W. (2023). Data-driven state observation for nonlinear systems based on online learning. AICHE JOURNAL, 8. https://doi.org/10.1002/aic.18224

Tang, W., & Daoutidis, P. (2023). Optimal Design of Control-Lyapunov Functions by Semi-Infinite Stochastic Programming. 2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC, pp. 7277–7284. https://doi.org/10.1109/CDC49753.2023.1038499

Tang, W., Allman, A., Mitrai, I., & Daoutidis, P. (2023). Resolving large-scale control and optimization through network structure analysis and decomposition: A tutorial review. 2023 AMERICAN CONTROL CONFERENCE, ACC, pp. 3113–3129. https://doi.org/10.23919/ACC55779.2023.10156560

Daoutidis, P., Megan, L., & Tang, W. (2023). The future of control of process systems. COMPUTERS & CHEMICAL ENGINEERING, 178. https://doi.org/10.1016/j.compchemeng.2023.108365

Tang, W., & Daoutidis, P. (2021). Coordinating distributed MPC efficiently on a plantwide scale: The Lyapunov envelope algorithm. Computers & Chemical Engineering, 155, 107532. https://doi.org/10.1016/j.compchemeng.2021.107532

Tang, W., & Daoutidis, P. (2021). Dissipativity learning control (DLC): Theoretical foundations of input–output data-driven model-free control. Systems & Control Letters, 147, 104831. https://doi.org/10.1016/j.sysconle.2020.104831

Tang, W., & Daoutidis, P. (2021). Fast and stable nonconvex constrained distributed optimization: the ELLADA algorithm. Optimization and Engineering, 23(1), 259–301. https://doi.org/10.1007/s11081-020-09585-w

Tang, W., & Daoutidis, P. (2021). Nonlinear state and parameter estimation using derivative information: A Lie-Sobolev approach. Computers & Chemical Engineering, 151, 107369. https://doi.org/10.1016/j.compchemeng.2021.107369

Mitrai, I., Tang, W., & Daoutidis, P. (2021). Stochastic blockmodeling for learning the structure of optimization problems. AIChE Journal, 68(6). https://doi.org/10.1002/aic.17415

Tang, W., & Daoutidis, P. (2019). A Bilevel Programming Approach to the Convergence Analysis of Control-Lyapunov Functions. IEEE Transactions on Automatic Control, 64(10), 4174–4179. https://doi.org/10.1109/TAC.2019.2892386

Allman, A., Tang, W., & Daoutidis, P. (2019). DeCODe: a community-based algorithm for generating high-quality decompositions of optimization problems. Optimization and Engineering, 20(4), 1067–1084. https://doi.org/10.1007/s11081-019-09450-5

Daoutidis, P., Tang, W., & Allman, A. (2019). Decomposition of control and optimization problems by network structure: Concepts, methods, and inspirations from biology. AIChE Journal, 65(10). https://doi.org/10.1002/aic.16708

Tang, W., & Daoutidis, P. (2019). Dissipativity learning control (DLC): A framework of input–output data-driven control. Computers & Chemical Engineering, 130, 106576. https://doi.org/10.1016/j.compchemeng.2019.106576

Tang, W., & Daoutidis, P. (2019). Distributed control and optimization of process system networks: A review and perspective. Chinese Journal of Chemical Engineering, 27(7), 1461–1473. https://doi.org/10.1016/j.cjche.2018.08.027

Daoutidis, P., Allman, A., Khatib, S., Moharir, M. A., Palys, M. J., Pourkargar, D. B., & Tang, W. (2019). Distributed decision making for intensified process systems. Current Opinion in Chemical Engineering, 25, 75–81. https://doi.org/10.1016/j.coche.2018.12.007

Constantino, P. H., Tang, W., & Daoutidis, P. (2019). Topology Effects on Sparse Control of Complex Networks with Laplacian Dynamics. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-45476-6

Daoutidis, P., Tang, W., & Jogwar, S. S. (2018). Decomposing complex plants for distributed control: Perspectives from network theory. Computers & Chemical Engineering, 114, 43–51. https://doi.org/10.1016/j.compchemeng.2017.10.015

Tang, W., & Daoutidis, P. (2018). Distributed adaptive dynamic programming for data-driven optimal control. Systems & Control Letters, 120, 36–43. https://doi.org/10.1016/j.sysconle.2018.08.002

Tang, W., & Daoutidis, P. (2018). Network decomposition for distributed control through community detection in input–output bipartite graphs. Journal of Process Control, 64, 7–14. https://doi.org/10.1016/j.jprocont.2018.01.009

Tang, W., Allman, A., Pourkargar, D. B., & Daoutidis, P. (2018). Optimal decomposition for distributed optimization in nonlinear model predictive control through community detection. Computers & Chemical Engineering, 111, 43–54. https://doi.org/10.1016/j.compchemeng.2017.12.010

Tang, W., Babaei Pourkargar, D., & Daoutidis, P. (2018). Relative time‐averaged gain array (RTAGA) for distributed control‐oriented network decomposition. AIChE Journal, 64(5), 1682–1690. https://doi.org/10.1002/aic.16130

Kang, L., Tang, W., Liu, Y., & Daoutidis, P. (2016). Control configuration synthesis using agglomerative hierarchical clustering: A graph-theoretic approach. Journal of Process Control, 46, 43–54. https://doi.org/10.1016/j.jprocont.2016.07.009