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Reference-shaping adaptive control by using gradient descent optimizers

This study presents a model reference adaptive control scheme based on reference-shaping approach. The proposed adaptive control structure includes two optimizer processes that perform gradient descent optimization. The first process is the control optimizer that generates appropriate control signal...

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Detalles Bibliográficos
Autores principales: Alagoz, Baris Baykant, Kavuran, Gurkan, Ates, Abdullah, Yeroglu, Celaleddin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5706737/
https://www.ncbi.nlm.nih.gov/pubmed/29186173
http://dx.doi.org/10.1371/journal.pone.0188527
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author Alagoz, Baris Baykant
Kavuran, Gurkan
Ates, Abdullah
Yeroglu, Celaleddin
author_facet Alagoz, Baris Baykant
Kavuran, Gurkan
Ates, Abdullah
Yeroglu, Celaleddin
author_sort Alagoz, Baris Baykant
collection PubMed
description This study presents a model reference adaptive control scheme based on reference-shaping approach. The proposed adaptive control structure includes two optimizer processes that perform gradient descent optimization. The first process is the control optimizer that generates appropriate control signal for tracking of the controlled system output to a reference model output. The second process is the adaptation optimizer that performs for estimation of a time-varying adaptation gain, and it contributes to improvement of control signal generation. Numerical update equations derived for adaptation gain and control signal perform gradient descent optimization in order to decrease the model mismatch errors. To reduce noise sensitivity of the system, a dead zone rule is applied to the adaptation process. Simulation examples show the performance of the proposed Reference-Shaping Adaptive Control (RSAC) method for several test scenarios. An experimental study demonstrates application of method for rotor control.
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spelling pubmed-57067372017-12-08 Reference-shaping adaptive control by using gradient descent optimizers Alagoz, Baris Baykant Kavuran, Gurkan Ates, Abdullah Yeroglu, Celaleddin PLoS One Research Article This study presents a model reference adaptive control scheme based on reference-shaping approach. The proposed adaptive control structure includes two optimizer processes that perform gradient descent optimization. The first process is the control optimizer that generates appropriate control signal for tracking of the controlled system output to a reference model output. The second process is the adaptation optimizer that performs for estimation of a time-varying adaptation gain, and it contributes to improvement of control signal generation. Numerical update equations derived for adaptation gain and control signal perform gradient descent optimization in order to decrease the model mismatch errors. To reduce noise sensitivity of the system, a dead zone rule is applied to the adaptation process. Simulation examples show the performance of the proposed Reference-Shaping Adaptive Control (RSAC) method for several test scenarios. An experimental study demonstrates application of method for rotor control. Public Library of Science 2017-11-29 /pmc/articles/PMC5706737/ /pubmed/29186173 http://dx.doi.org/10.1371/journal.pone.0188527 Text en © 2017 Alagoz et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Alagoz, Baris Baykant
Kavuran, Gurkan
Ates, Abdullah
Yeroglu, Celaleddin
Reference-shaping adaptive control by using gradient descent optimizers
title Reference-shaping adaptive control by using gradient descent optimizers
title_full Reference-shaping adaptive control by using gradient descent optimizers
title_fullStr Reference-shaping adaptive control by using gradient descent optimizers
title_full_unstemmed Reference-shaping adaptive control by using gradient descent optimizers
title_short Reference-shaping adaptive control by using gradient descent optimizers
title_sort reference-shaping adaptive control by using gradient descent optimizers
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5706737/
https://www.ncbi.nlm.nih.gov/pubmed/29186173
http://dx.doi.org/10.1371/journal.pone.0188527
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