Input design for the optimal control of networked moments

Fuente: arXiv
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Autori principali: Solimine, Philip, Meyer-Baese, Anke
Natura: Preprint
Pubblicazione: 2021
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author Solimine, Philip
Meyer-Baese, Anke
author_facet Solimine, Philip
Meyer-Baese, Anke
contents We study the optimal control of the mean and variance of the network state vector. We develop an algorithm that uses projected gradient descent to optimize the control input placement, subject to constraints on the state that must be achieved at a given time threshold; seeking to design an input that moves the moment at minimum cost. First, we solve the state-selection problem for a number of variants of the first and second moment, and find solutions related to the eigenvalues of the systems' Gramian matrices. We then nest this state selection into projected gradient descent to design optimal inputs.
format Preprint
id arxiv_https___arxiv_org_abs_2106_05265
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Input design for the optimal control of networked moments
Solimine, Philip
Meyer-Baese, Anke
Optimization and Control
Social and Information Networks
Systems and Control
49M99, 93B05, 93B70, 90C99
We study the optimal control of the mean and variance of the network state vector. We develop an algorithm that uses projected gradient descent to optimize the control input placement, subject to constraints on the state that must be achieved at a given time threshold; seeking to design an input that moves the moment at minimum cost. First, we solve the state-selection problem for a number of variants of the first and second moment, and find solutions related to the eigenvalues of the systems' Gramian matrices. We then nest this state selection into projected gradient descent to design optimal inputs.
title Input design for the optimal control of networked moments
topic Optimization and Control
Social and Information Networks
Systems and Control
49M99, 93B05, 93B70, 90C99
url https://arxiv.org/abs/2106.05265