Wentao Tang

Identification of noisy Koopman models

Woelk, M., & Tang, W. (2025, September 8). Computers & Chemical Engineering.

Koopman-Nemytskii Operator: A Linear Representation of Nonlinear Controlled Systems

Tang, W. (2025, March 24). ArXiv (Cornell University).

Learning the Integral Quadratic Constraints on Plant-Model Mismatch

Tang, W. (2025, February 3). ArXiv (Cornell University).

Process Resilience under Optimal Data Injection Attacks

Ye, X., & Tang, W. (2025, May 13). AIChE Journal, Vol. 5.

Data-Driven Bifurcation Analysis via Learning of Homeomorphism

Proceedings of Machine Learning Research, 242, 1149–1160. http://www.scopus.com/inward/record.url?eid=2-s2.0-85203701049&partnerID=MN8TOARS

Data-Driven Nonlinear State Observation using Video Measurements

Weeks, C., & Tang, W. (2024, January 1). IFAC-PapersOnLine, Vol. 58, pp. 787–792.

Data‐driven nonlinear state observation for controlled systems: A kernel method and its analysis

Woelk, M., & Tang, W. (2024, July 8). The Canadian Journal of Chemical Engineering, Vol. 7, pp. 1122–1138.

High-throughput design of complex oxides as isothermal, redox-activated CO₂ sorbents for green hydrogen generation

Energy & Environmental Science, 17(17), 6279–6290.

Koopman Operator in the Weighted Function Spaces and its Learning for the Estimation of Lyapunov and Zubov Functions

Tang, W. (2024, September 30). ArXiv (Cornell University).

Synthesis of Data-Driven Nonlinear State Observers using Lipschitz-Bounded Neural Networks

Tang, W. (2024, July 10). Proceedings of the American Control Conference, pp. 1713–1719.

Systematic MPC tuning with direct response shaping: Parameterization and Inverse optimization-based Tuning Approach (PITA)

Tang, W. (2024, October 7). Control Engineering Practice, Vol. 153.

Automatic decomposition of large-scale industrial processes for distributed MPC on the Shell–Yokogawa Platform for Advanced Control and Estimation (PACE)

Tang, W., Carrette, P., Cai, Y., Williamson, J. M., & Daoutidis, P. (2023, August 7). Computers & Chemical Engineering, Vol. 178.

Data-Driven Bifurcation Analysis via Learning of Homeomorphism

Tang, W. (2023, December 11). ArXiv (Cornell University).

Data-Driven Nonlinear State Observation using Video Measurements

Weeks, C., & Tang, W. (2023, November 25). ArXiv (Cornell University).

Data-Driven State Observation for Nonlinear Systems based on Online Learning

Tang, W. (2023, May 10).

Data‐driven state observation for nonlinear systems based on online learning

Tang, W. (2023, August 28). AIChE Journal, Vol. 8.

Dissipativity Learning Control through Estimation from Online Trajectories^*

Tang, W., & Woelk, M. (2023, May 31). Proceedings of the American Control Conference, pp. 3036–3041.

Optimal Design of Control-Lyapunov Functions by Semi-Infinite Stochastic Programming

2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC, pp. 7277–7284.

Optimal Design of Control-Lyapunov Functions by Semi-Infinite Stochastic Programming

Tang, W., & Daoutidis, P. (2023, December 13). Proceedings of the IEEE Conference on Decision and Control, pp. 7277–7284.

Resolving large-scale control and optimization through network structure analysis and decomposition: A tutorial review

Tang, W., Allman, A., Mitrai, I., & Daoutidis, P. (2023, May 31). 2023 AMERICAN CONTROL CONFERENCE, ACC, pp. 3113–3129.

Synthesis of Data-Driven Nonlinear State Observers using Lipschitz-Bounded Neural Networks

Tang, W. (2023, October 4). ArXiv (Cornell University).

The future of control of process systems

Daoutidis, P., Megan, L., & Tang, W. (2023, July 25). Computers & Chemical Engineering, Vol. 178.

Data-Driven Control: Overview and Perspectives

Tang, W., & Daoutidis, P. (2022, June 8). 2022 American Control Conference (ACC), pp. 1048–1064.

Coordinating distributed MPC efficiently on a plantwide scale: The Lyapunov envelope algorithm

Computers & Chemical Engineering, 155, 107532.

Fast and stable nonconvex constrained distributed optimization: the ELLADA algorithm

Optimization and Engineering, 23(1), 259–301.

Nonlinear state and parameter estimation using derivative information: A Lie-Sobolev approach

Computers & Chemical Engineering, 151, 107369.

Stochastic blockmodeling for learning the structure of optimization problems

AIChE Journal, 68(6).

Dissipativity learning control (DLC): Theoretical foundations of input–output data-driven model-free control

Systems & Control Letters, 147, 104831.

Fast and Stable Nonconvex Constrained Distributed Optimization: The ELLADA Algorithm

Tang, W., & Daoutidis, P. (2020, April 4). ArXiv (Cornell University).

A Bilevel Programming Approach to the Convergence Analysis of Control-Lyapunov Functions

IEEE Transactions on Automatic Control, 64(10), 4174–4179.

Automated structure-based decomposition and decomposition-based solution of optimization problems

AIChE Annual Meeting, Conference Proceedings, 2019-November. http://www.scopus.com/inward/record.url?eid=2-s2.0-85095692437&partnerID=MN8TOARS

Data-driven dissipativity-based control

AIChE Annual Meeting, Conference Proceedings, 2019-November. http://www.scopus.com/inward/record.url?eid=2-s2.0-85095694525&partnerID=MN8TOARS

DeCODe: a community-based algorithm for generating high-quality decompositions of optimization problems

Optimization and Engineering, 20(4), 1067–1084.

Decomposition of control and optimization problems by network structure: Concepts, methods, and inspirations from biology

AIChE Journal, 65(10).

Dissipativity learning control (DLC): A framework of input–output data-driven control

Computers & Chemical Engineering, 130, 106576.

Distributed decision making for intensified process systems

Current Opinion in Chemical Engineering, 25, 75–81.

Distributed nonlinear model predictive control through accelerated parallel ADMM

Tang, W., & Daoutidis, P. (2019, July 1). Proceedings of the American Control Conference, pp. 1406–1411.

Evolution of modular networks through optimal sparse control

AIChE Annual Meeting, Conference Proceedings, 2019-November. http://www.scopus.com/inward/record.url?eid=2-s2.0-85095697181&partnerID=MN8TOARS

Input-output data-driven control through dissipativity learning

Tang, W., & Daoutidis, P. (2019, July 1). Proceedings of the American Control Conference, pp. 4217–4222.

Lyapunov Dynamic Flexibility of Nonlinear Processes

Tang, W., & Daoutidis, P. (2019, January 1). Computer-Aided Chemical Engineering/Computer Aided Chemical Engineering, Vol. 47, pp. 35–40.

Lyapunov stability and process flexibility analysis analogies and new approaches

AIChE Annual Meeting, Conference Proceedings, 2019-November. http://www.scopus.com/inward/record.url?eid=2-s2.0-85095689989&partnerID=MN8TOARS

Optimal sparse network topology under sparse control in Laplacian networks

Tang, W., Constantino, P. H., & Daoutidis, P. (2019, January 1). IFAC-PapersOnLine, Vol. 52, pp. 273–278.

Topology Effects on Sparse Control of Complex Networks with Laplacian Dynamics

Scientific Reports, 9(1).

Distributed adaptive dynamic programming for data-driven optimal control

Systems & Control Letters, 120, 36–43.

Distributed control and optimization of process system networks: A review and perspective

Chinese Journal of Chemical Engineering, 27(7), 1461–1473.

Network decomposition for distributed control through community detection in input–output bipartite graphs

Journal of Process Control, 64, 7–14.

Relative time‐averaged gain array (RTAGA) for distributed control‐oriented network decomposition

AIChE Journal, 64(5), 1682–1690.

Reprint of: Optimal decomposition for distributed optimization in nonlinear model predictive control through community detection

Tang, W., Allman, A., Pourkargar, D. B., & Daoutidis, P. (2018, August 1). Computers & Chemical Engineering, Vol. 116, pp. 144–155.

The role of community structures in sparse feedback control

Tang, W., & Daoutidis, P. (2018, June 1). Proceedings of the American Control Conference, pp. 1790–1795.

Towards a Generic Algorithm for Identifying High-Quality Decompositions of Optimization Problems

Allman, A., Tang, W., & Daoutidis, P. (2018, January 1). Computer-Aided Chemical Engineering/Computer Aided Chemical Engineering, Vol. 44, pp. 943–948.

Decomposing complex plants for distributed control: Perspectives from network theory

Computers & Chemical Engineering, 114, 43–51.

Distributed/Hierarchical Control Architecture Design

Tang, W., & Daoutidis, P. (2017, July 1). IFAC-PapersOnLine, Vol. 50, pp. 12015–12020.

Optimal decomposition for distributed optimization in nonlinear model predictive control through community detection

Computers & Chemical Engineering, 111, 43–54.

Relative time-averaged gain array for distributed control architecture design

Computing and Systems Technology Division 2017 - Core Programming Area at the 2017 AIChE Annual Meeting, 2017-October, 456–464. http://www.scopus.com/inward/record.url?eid=2-s2.0-85053110245&partnerID=MN8TOARS

The role of community structures in network control: A case for the evolution of modular networks in biology

Computing and Systems Technology Division 2017 - Core Programming Area at the 2017 AIChE Annual Meeting, 2017-October, 263–270. http://www.scopus.com/inward/record.url?eid=2-s2.0-85053106820&partnerID=MN8TOARS

Control configuration selection using agglomerative hierarchical clustering graph theoretical approach

Computing and Systems Technology Division 2016 - Core Programming Area at the 2016 AIChE Annual Meeting, 274–275. http://www.scopus.com/inward/record.url?eid=2-s2.0-85019134429&partnerID=MN8TOARS

Control configuration synthesis using agglomerative hierarchical clustering: A graph-theoretic approach

Journal of Process Control, 46, 43–54.