Distributed Estimation and Nonlinear Model Predictive Control Using Community Detection

Pourkargar, Davood Babaei; Moharir, Manjiri; Almansoori, Ali; Daoutidis, Pródromos

doi:10.1021/acs.iecr.9b00820

Cited by 33 publications

(30 citation statements)

References 37 publications

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“…These works also demonstrated computational advantages of community-based decompositions. When state information is not fully available, a state observer is needed to combine with the controller; in this case, Yin & Liu 109 adopted an integrated community detection and stabilizability and observability test procedure, while Pourkargar et al 110 proposed to determine the control-oriented and observation-oriented decompositions (which may not coincide) by community detection in a input-state digraph and a stateoutput digraph, respectively.…”

Section: Modularity-based Decomposition For Distributed Control and Optimizationmentioning

confidence: 99%

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

2019

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Section: Modularity-based Decomposition For Distributed Control and Optimizationmentioning

confidence: 99%

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

2019

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“…The argument behind this measure is that modular organizations that arise in natural systems are nonrandom. This measure is intuitive and has seen many interesting applications; for instance, this measure has been shown to provide a flexible and powerful tool for the analysis and design of control architectures and for the decomposition of large‐scale optimization problems . A powerful generalization of Newman's measure has been proposed in Reference and here it was shown that systems of high modularity are extremum points of a Hamiltonian function.…”

Section: Introductionmentioning

confidence: 98%

Modularity measures: Concepts, computation, and applications to manufacturing systems

Shao

Zavala

2020

AIChE Journal

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We propose a measure to quantify the modularity of industrial production (manufacturing) systems and optimization formulations to compute it. From a manufacturing perspective, we argue that a system is deemed modular if: (a) the equipment units that comprise it form clusters (modules) of dense connectivity (i.e., difficult module assembly tasks are performed off-site), (b) connectivity between modules is sparse (i.e., easy assembly tasks are performed on-site), (c) the number of modules is small, and (d) the module dimensions facilitate transportation. We show that the measure proposed satisfies these requirements and that it can be computed by solving a convex mixed-integer quadratic program. We provide a discussion on advantages and disadvantages of alternative modularity measures used in different scientific and engineering communities. Our results seek to highlight conceptual and computational challenges that arise from the need to define and quantify modularity in a manufacturing context.

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“…Real‐world networks are complex and large scale, represent very interacting entities, and generate diverse types of data. Biological networks, 1 power networks, 2 controlled chemical processes, 3‐6 and many others 7‐9 are examples of complex networks. Moreover, modern manufacturing plants are increasingly integrated, 10,11 leading to structural and computational complexities.…”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm for community detection in complex weighted networks

et al. 2021

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Community detection decomposes large‐scale, complex networks “optimally” into sets of smaller sub‐networks. It finds sub‐networks that have the least inter‐connections and the most intra‐connections. This article presents an efficient community detection algorithm that detects community structures in a weighted network by solving a multi‐objective optimization problem. The whale optimization algorithm is extended to enable it to handle multi‐objective optimization problems with discrete variables and to solve the problems on parallel processors. To this end, the population's positions are discretized using a transfer function that maps real variables to discrete variables, the initialization steps for the algorithm are modified to prevent generating unrealistic connections between variables, and the updating step of the algorithm is redefined to produce integer numbers. To identify the community configurations that are Pareto optimal, the non‐dominated sorting concept is adopted. The proposed algorithm is tested on the Tennessee Eastman process and several benchmark community‐detection problems.

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Distributed Estimation and Nonlinear Model Predictive Control Using Community Detection

Cited by 33 publications

References 37 publications

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

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

Modularity measures: Concepts, computation, and applications to manufacturing systems

An efficient algorithm for community detection in complex weighted networks

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