Wentao Tang
Nonlinear control, Distributed optimization, Data-driven control, Process control, Nonlinear optimization
Wentao Tang was born in Hunan Province, P. R. China. He received his B.S. degree in Chemical Engineering with a secondary degree in Mathematics and Applied Mathematics from Tsinghua University, Beijing, in 2015, and his Ph. D. degree in Chemical Engineering at University of Minnesota, Minneapolis, in 2020. He worked as a Process Control Engineer at Shell Global Solutions (U.S.) Inc., leading multiple R&D projects for the development of Shell’s advanced process control software — Platform of Advanced Control and Estimation (PACE), from 2020 to 2022. He joined the NC State University as an Assistant Professor since August 2022. He has authored over 20 journal papers and over 10 conference proceedings papers. He was a recipient of Doctoral Dissertation Fellowship of University of Minnesota for 2018—2019, and the 1st place in CAST Directors' Student Presentation Award of the 2019 AIChE Annual Meeting. His current research interests include data-driven control through machine learning, structured and scalable algorithms for distributed and black-box optimization, as well as decision making for large-scale and multi-scale systems.
Works (25)
Updated: August 1st, 2024 05:08
2024 article
Data-driven nonlinear state observation for controlled systems: A kernel method and its analysis
Woelk, M., & Tang, W. (2024, July 8). CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Vol. 7.
author keywords: canonical correlation; data-driven dynamics; kernel methods; nonlinear systems; state observation
open access via doi.org (publisher)
Sources: Web Of Science, NC State University Libraries
Added: July 22, 2024
2023 journal article
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.
author keywords: Network decomposition; Community detection; Model predictive control; Plantwide control
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure
(OpenAlex)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: September 18, 2023
2023 article
Data-Driven State Observation for Nonlinear Systems based on Online Learning
Tang, W. (2023, May 10).
Source: ORCID
Added: February 1, 2024
2023 journal article
Data-driven state observation for nonlinear systems based on online learning
AICHE JOURNAL, 8.
author keywords: dimensionality reduction; nonlinear systems; online optimization; state observer
TL;DR:
An efficient model‐free, data-driven approach for state observation is proposed, which is suitable for data‐driven nonlinear control without accurate nonlinear models.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: September 11, 2023
2023 article
Optimal Design of Control-Lyapunov Functions by Semi-Infinite Stochastic Programming
2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC, pp. 7277–7284.
Sources: Web Of Science, NC State University Libraries
Added: March 25, 2024
2023 article
Resolving large-scale control and optimization through network structure analysis and decomposition: A tutorial review
2023 AMERICAN CONTROL CONFERENCE, ACC, pp. 3113–3129.
TL;DR:
A tutorial review of how to decompose a network representing a control or optimization problem according to its latent block structure, how decomposition is determined for distributed control, and how optimization problems are solved under decomposition are given.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: March 11, 2024
2023 journal article
The future of control of process systems
COMPUTERS & CHEMICAL ENGINEERING, 178.
author keywords: Process systems; Control theory; Control technology; Optimization
TL;DR:
Large scale systems control and identification, nonlinear model-based and model-free control, and controller performance monitoring and diagnosis are discussed as major directions for future research, along with control technology and industry workforce challenges and opportunities.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure
(OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: September 5, 2023
2021 journal article
Coordinating distributed MPC efficiently on a plantwide scale: The Lyapunov envelope algorithm
Computers & Chemical Engineering, 155, 107532.
TL;DR:
This work proposes a splitting algorithm for distributed optimization analogous to forward-backward splitting (FBS), where l 1 and quadratic penalties are imposed on the violation of interconnecting relations among the subsystems, thus maintaining closed-loop stability under distributed MPC despite early termination and yielding improving control performance as the allowed computational time or number of iterations increases.
(via Semantic Scholar)
Sources: Crossref, NC State University Libraries
Added: March 19, 2023
2021 journal article
Dissipativity learning control (DLC): Theoretical foundations of input–output data-driven model-free control
Systems & Control Letters, 147, 104831.
TL;DR:
The statistical conditions on dissipativity learning that enable control performance guarantees are analyzed, and theoretical results on performance under nominal conditions as well as in the presence of statistical errors are established.
(via Semantic Scholar)
Sources: Crossref, NC State University Libraries, ORCID
Added: March 18, 2023
2021 journal article
Fast and stable nonconvex constrained distributed optimization: the ELLADA algorithm
Optimization and Engineering, 23(1), 259–301.
TL;DR:
An extra-layer architecture is adopted to accommodate nonconvexity and handle inequality constraints, and a modified Anderson acceleration is employed for reducing the number of iterations of the proposed algorithm, named ELLADA.
(via Semantic Scholar)
Sources: ORCID, Crossref, NC State University Libraries
Added: March 19, 2023
2021 journal article
Nonlinear state and parameter estimation using derivative information: A Lie-Sobolev approach
Computers & Chemical Engineering, 151, 107369.
Sources: Crossref, NC State University Libraries
Added: March 19, 2023
2021 journal article
Stochastic blockmodeling for learning the structure of optimization problems
AIChE Journal, 68(6).
Sources: ORCID, Crossref, NC State University Libraries
Added: March 19, 2023
2019 journal article
A Bilevel Programming Approach to the Convergence Analysis of Control-Lyapunov Functions
IEEE Transactions on Automatic Control, 64(10), 4174–4179.
TL;DR:
This paper formulate the estimation problems as min–max bilevel programs for the decay rate of the Lyapunov function, where the inner problem can be resolved using Karush–Kuhn–Tucker optimality conditions, and the resulting single-level programs can be transformed into and solved as mixed-integer nonlinear programs.
(via Semantic Scholar)
Sources: Crossref, NC State University Libraries, ORCID
Added: March 18, 2023
2019 journal article
DeCODe: a community-based algorithm for generating high-quality decompositions of optimization problems
Optimization and Engineering, 20(4), 1067–1084.
TL;DR:
The ability of DeCODe to identify nontrivial decompositions of optimization problems is demonstrated through a large renewable energy and chemical production optimal design problem and two mixed integer nonlinear program test problems.
(via Semantic Scholar)
Sources: Crossref, NC State University Libraries
Added: March 18, 2023
2019 journal article
Decomposition of control and optimization problems by network structure: Concepts, methods, and inspirations from biology
AIChE Journal, 65(10).
Sources: Crossref, NC State University Libraries
Added: March 19, 2023
2019 journal article
Dissipativity learning control (DLC): A framework of input–output data-driven control
Computers & Chemical Engineering, 130, 106576.
TL;DR:
A dissipativity learning control framework which involves the data-based learning of the dissipativity property of the control system, followed by a dissipativity-based controller design procedure is proposed.
(via Semantic Scholar)
Sources: Crossref, NC State University Libraries
Added: March 19, 2023
2019 journal article
Distributed control and optimization of process system networks: A review and perspective
Chinese Journal of Chemical Engineering, 27(7), 1461–1473.
TL;DR:
This paper provides a comprehensive, up-to-date review of algorithms and methods for distributed control and optimization with perspectives and discussions on possible future directions.
(via Semantic Scholar)
Sources: Crossref, NC State University Libraries
Added: March 19, 2023
2019 journal article
Distributed decision making for intensified process systems
Current Opinion in Chemical Engineering, 25, 75–81.
TL;DR:
A summary of recent developments and future directions in distributed decision making for intensified systems, specifically with respect to optimization, control and monitoring, with emphasis on methods for obtaining high quality decompositions for such problems based on network theory is given.
(via Semantic Scholar)
Sources: Crossref, NC State University Libraries
Added: March 18, 2023
2019 journal article
Topology Effects on Sparse Control of Complex Networks with Laplacian Dynamics
Scientific Reports, 9(1).
TL;DR:
Although sparse and heterogeneous undirected networks may require larger numbers of actuators and sensors for structural controllability, networks with Laplacian dynamics are shown to be easier to control when accounting for the cost of feedback channels.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy
(OpenAlex)
Sources: Crossref, NC State University Libraries
Added: March 18, 2023
2018 journal article
Decomposing complex plants for distributed control: Perspectives from network theory
Computers & Chemical Engineering, 114, 43–51.
TL;DR:
This paper reviews recent research on the application of methods from the theory of networks for developing distributed control architectures for complex plants based on hierarchical clustering and modularity optimization.
(via Semantic Scholar)
Sources: Crossref, NC State University Libraries
Added: March 19, 2023
2018 journal article
Distributed adaptive dynamic programming for data-driven optimal control
Systems & Control Letters, 120, 36–43.
TL;DR:
This work identifies the data-driven ADP problem as a consensus optimization problem for nonlinear affine systems, and applies the alternating direction method of multipliers (ADMM) and its accelerated variants for its solution.
(via Semantic Scholar)
Sources: Crossref, NC State University Libraries
Added: March 19, 2023
2018 journal article
Network decomposition for distributed control through community detection in input–output bipartite graphs
Journal of Process Control, 64, 7–14.
TL;DR:
A novel weighted input– Output bipartite graph representation of control systems is constructed, in which the input–output edge weights capture topological connectivity and short-time response sensitivities.
(via Semantic Scholar)
Sources: Crossref, NC State University Libraries
Added: March 19, 2023
2018 journal article
Optimal decomposition for distributed optimization in nonlinear model predictive control through community detection
Computers & Chemical Engineering, 111, 43–54.
TL;DR:
This work proposes to use community detection in network representations of optimization problems as a systematic method of partitioning the optimization variables into groups, such that the variables in the same groups generally share more constraints than variables between different groups.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions
(OpenAlex)
Sources: Crossref, NC State University Libraries
Added: March 19, 2023
2018 journal article
Relative time‐averaged gain array (RTAGA) for distributed control‐oriented network decomposition
AIChE Journal, 64(5), 1682–1690.
Sources: Crossref, NC State University Libraries
Added: March 18, 2023
2016 journal article
Control configuration synthesis using agglomerative hierarchical clustering: A graph-theoretic approach
Journal of Process Control, 46, 43–54.
TL;DR:
A recently developed agglomerative hierarchical clustering approach is reformulated in a graph theoretic setting, allowing the efficient generation of decentralized control configuration as well as the entire hierarchy of block decentralized control configurations.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy
(OpenAlex)
Sources: Crossref, NC State University Libraries
Added: March 19, 2023
Employment
Updated: May 15th, 2022 12:17
2022 - present
North Carolina State University Raleigh, North Carolina, US
Assistant Professor
Department of Chemical and Biomolecular Engineering
2020 - 2022
Shell Global Solutions USA Inc Houston, TX, US
Process Control Engineer, Domain Expert
Projects and Technology
2015 - 2020
University of Minnesota Minneapolis, MN, US
Research Assistant
Department of Chemical Engineering and Materials Science
2019 - 2019
ExxonMobil Research and Engineering Company Spring, TX, US
Research Engineer (intern)
Advanced Control Section
Education
Updated: May 15th, 2022 12:14
2015 - 2020
University of Minnesota Minneapolis, MN, US
Ph.D.
Department of Chemical Engineering and Materials Science
2011 - 2015
Tsinghua University Beijing, Beijing, CN
B.S.
Department of Chemical Engineering
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