| Optimal decomposition for distributed optimization in nonlinear model predictive control through community detection W Tang, A Allman, DB Pourkargar, P Daoutidis Computers & Chemical Engineering 111, 43-54, 2018 | 68 | 2018 |
| Decomposing complex plants for distributed control: Perspectives from network theory P Daoutidis, W Tang, SS Jogwar Computers & Chemical Engineering 114, 43-51, 2018 | 54 | 2018 |
| Network decomposition for distributed control through community detection in input–output bipartite graphs W Tang, P Daoutidis Journal of Process Control 64, 7-14, 2018 | 48* | 2018 |
| Control configuration synthesis using agglomerative hierarchical clustering: A graph-theoretic approach L Kang, W Tang, Y Liu, P Daoutidis Journal of Process Control 46, 43-54, 2016 | 40 | 2016 |
| Decomposition of control and optimization problems by network structure: Concepts, methods, and inspirations from biology P Daoutidis, W Tang, A Allman AIChE Journal 65 (10), e16708, 2019 | 34 | 2019 |
| Distributed adaptive dynamic programming for data-driven optimal control W Tang, P Daoutidis Systems & Control Letters 120, 36-43, 2018 | 33 | 2018 |
| Relative time‐averaged gain array (RTAGA) for distributed control‐oriented network decomposition W Tang, D Babaei Pourkargar, P Daoutidis AIChE Journal 64 (5), 1682-1690, 2018 | 29 | 2018 |
| Fast and stable nonconvex constrained distributed optimization: the ELLADA algorithm W Tang, P Daoutidis Optimization and Engineering 23 (1), 259-301, 2022 | 27 | 2022 |
| DeCODe: a community-based algorithm for generating high-quality decompositions of optimization problems A Allman, W Tang, P Daoutidis Optimization and Engineering 20, 1067-1084, 2019 | 27 | 2019 |
| Dissipativity learning control (DLC): A framework of input–output data-driven control W Tang, P Daoutidis Computers & Chemical Engineering 130, 106576, 2019 | 27 | 2019 |
| Data-Driven Control: Overview and Perspectives * W Tang, P Daoutidis 2022 American Control Conference (ACC), 1048-1064, 2022 | 22 | 2022 |
| Distributed nonlinear model predictive control through accelerated parallel ADMM W Tang, P Daoutidis 2019 American Control Conference (ACC), 1406-1411, 2019 | 21 | 2019 |
| Topology effects on sparse control of complex networks with Laplacian dynamics PH Constantino, W Tang, P Daoutidis Scientific reports 9 (1), 9034, 2019 | 21 | 2019 |
| Dissipativity learning control (DLC): theoretical foundations of input–output data-driven model-free control W Tang, P Daoutidis Systems & Control Letters 147, 104831, 2021 | 19 | 2021 |
| Distributed decision making for intensified process systems P Daoutidis, A Allman, S Khatib, MA Moharir, MJ Palys, DB Pourkargar, ... Current Opinion in Chemical Engineering 25, 75-81, 2019 | 17 | 2019 |
| Distributed control and optimization of process system networks: A review and perspective W Tang, P Daoutidis Chinese Journal of Chemical Engineering 27 (7), 1461-1473, 2019 | 15 | 2019 |
| Towards a generic algorithm for identifying high-quality decompositions of optimization problems A Allman, W Tang, P Daoutidis Computer Aided Chemical Engineering 44, 943-948, 2018 | 15 | 2018 |
| Stochastic blockmodeling for learning the structure of optimization problems I Mitrai, W Tang, P Daoutidis AIChE Journal 68 (6), e17415, 2022 | 12 | 2022 |
| Distributed/hierarchical control architecture design W Tang, P Daoutidis IFAC-PapersOnLine 50 (1), 12015-12020, 2017 | 11 | 2017 |
| The role of community structures in sparse feedback control W Tang, P Daoutidis 2018 Annual American Control Conference (ACC), 1790-1795, 2018 | 10 | 2018 |
Prodromos DaoutidisProfessor, Universiy of MinnesotaVerified email at umn.edu
