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

Tang, Wen; Allman, Andrew; Pourkargar, Davood Babaei; Daoutidis, Pródromos

doi:10.1016/j.compchemeng.2017.12.010

Cited by 58 publications

(40 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 98%

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

Shao

Zavala

2020

AIChE Journal

View full text Add to dashboard Cite

show abstract

“…In the upper layer, a supervisor checks and re-balances, if it is needed, the local control actions to guarantee global disturbance rejection. Tang et al (2018) propose community detection as a tool to partition the system when using NMPC. Maestre and Ishii (2017) present a fully distributed method where the cooperation architecture is given by link requests/offers between local controllers; and in (Fele et al, 2018), agents perform an autonomous negotiation protocol to determine the partition of the system using coalitional MPC.…”

Section: Introductionmentioning

confidence: 99%

A light clustering model predictive control approach to maximize thermal power in solar parabolic-trough plants

et al. 2021

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 58 publications

References 60 publications

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

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

A light clustering model predictive control approach to maximize thermal power in solar parabolic-trough plants

An efficient algorithm for community detection in complex weighted networks

Contact Info

Product

Resources

About