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Implementation of ANN-Based Auto-Adjustable for a Pneumatic Servo System Embedded on FPGA
Artificial intelligence techniques for pneumatic robot manipulators have become of deep interest in industrial applications, such as non-high voltage environments, clean operations, and high power-to-weight ratio tasks. The principal advantages of this type of actuator are the implementation of clea...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228457/ https://www.ncbi.nlm.nih.gov/pubmed/35744504 http://dx.doi.org/10.3390/mi13060890 |
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author | Cabrera-Rufino, Marco-Antonio Ramos-Arreguín, Juan-Manuel Rodríguez-Reséndiz, Juvenal Gorrostieta-Hurtado, Efren Aceves-Fernandez, Marco-Antonio |
author_facet | Cabrera-Rufino, Marco-Antonio Ramos-Arreguín, Juan-Manuel Rodríguez-Reséndiz, Juvenal Gorrostieta-Hurtado, Efren Aceves-Fernandez, Marco-Antonio |
author_sort | Cabrera-Rufino, Marco-Antonio |
collection | PubMed |
description | Artificial intelligence techniques for pneumatic robot manipulators have become of deep interest in industrial applications, such as non-high voltage environments, clean operations, and high power-to-weight ratio tasks. The principal advantages of this type of actuator are the implementation of clean energies, low cost, and easy maintenance. The disadvantages of working with pneumatic actuators are that they have non-linear characteristics. This paper proposes an intelligent controller embedded in a programmable logic device to minimize the non-linearities of the air behavior into a 3-degrees-of-freedom robot with pneumatic actuators. In this case, the device is suitable due to several electric valves, direct current motors signals, automatic controllers, and several neural networks. For every degree of freedom, three neurons adjust the gains for each controller. The learning process is constantly tuning the gain value to reach the minimum of the mean square error. Results plot a more appropriate behavior for a transitive time when the neurons work with the automatic controllers with a minimum mean error of [Formula: see text] mm. |
format | Online Article Text |
id | pubmed-9228457 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92284572022-06-25 Implementation of ANN-Based Auto-Adjustable for a Pneumatic Servo System Embedded on FPGA Cabrera-Rufino, Marco-Antonio Ramos-Arreguín, Juan-Manuel Rodríguez-Reséndiz, Juvenal Gorrostieta-Hurtado, Efren Aceves-Fernandez, Marco-Antonio Micromachines (Basel) Article Artificial intelligence techniques for pneumatic robot manipulators have become of deep interest in industrial applications, such as non-high voltage environments, clean operations, and high power-to-weight ratio tasks. The principal advantages of this type of actuator are the implementation of clean energies, low cost, and easy maintenance. The disadvantages of working with pneumatic actuators are that they have non-linear characteristics. This paper proposes an intelligent controller embedded in a programmable logic device to minimize the non-linearities of the air behavior into a 3-degrees-of-freedom robot with pneumatic actuators. In this case, the device is suitable due to several electric valves, direct current motors signals, automatic controllers, and several neural networks. For every degree of freedom, three neurons adjust the gains for each controller. The learning process is constantly tuning the gain value to reach the minimum of the mean square error. Results plot a more appropriate behavior for a transitive time when the neurons work with the automatic controllers with a minimum mean error of [Formula: see text] mm. MDPI 2022-05-31 /pmc/articles/PMC9228457/ /pubmed/35744504 http://dx.doi.org/10.3390/mi13060890 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cabrera-Rufino, Marco-Antonio Ramos-Arreguín, Juan-Manuel Rodríguez-Reséndiz, Juvenal Gorrostieta-Hurtado, Efren Aceves-Fernandez, Marco-Antonio Implementation of ANN-Based Auto-Adjustable for a Pneumatic Servo System Embedded on FPGA |
title | Implementation of ANN-Based Auto-Adjustable for a Pneumatic Servo System Embedded on FPGA |
title_full | Implementation of ANN-Based Auto-Adjustable for a Pneumatic Servo System Embedded on FPGA |
title_fullStr | Implementation of ANN-Based Auto-Adjustable for a Pneumatic Servo System Embedded on FPGA |
title_full_unstemmed | Implementation of ANN-Based Auto-Adjustable for a Pneumatic Servo System Embedded on FPGA |
title_short | Implementation of ANN-Based Auto-Adjustable for a Pneumatic Servo System Embedded on FPGA |
title_sort | implementation of ann-based auto-adjustable for a pneumatic servo system embedded on fpga |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228457/ https://www.ncbi.nlm.nih.gov/pubmed/35744504 http://dx.doi.org/10.3390/mi13060890 |
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