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Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor
Automated industrial processes require a controller to obtain an output signal similar to the reference indicated by the user. There are controllers such as PIDs, which are efficient if the system does not change its initial conditions. However, if this is not the case, the controller must be retune...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10527306/ https://www.ncbi.nlm.nih.gov/pubmed/37754185 http://dx.doi.org/10.3390/biomimetics8050434 |
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author | Alejandro-Sanjines, Ulbio Maisincho-Jivaja, Anthony Asanza, Victor Lorente-Leyva, Leandro L. Peluffo-Ordóñez, Diego H. |
author_facet | Alejandro-Sanjines, Ulbio Maisincho-Jivaja, Anthony Asanza, Victor Lorente-Leyva, Leandro L. Peluffo-Ordóñez, Diego H. |
author_sort | Alejandro-Sanjines, Ulbio |
collection | PubMed |
description | Automated industrial processes require a controller to obtain an output signal similar to the reference indicated by the user. There are controllers such as PIDs, which are efficient if the system does not change its initial conditions. However, if this is not the case, the controller must be retuned, affecting production times. In this work, an adaptive PID controller is developed for a DC motor speed plant using an artificial intelligence algorithm based on reinforcement learning. This algorithm uses an actor–critic agent, where its objective is to optimize the actor’s policy and train a critic for rewards. This will generate the appropriate gains without the need to know the system. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) was used, with a network composed of 300 neurons for the agent’s learning. Finally, the performance of the obtained controller is compared with a classical control one using a cost function. |
format | Online Article Text |
id | pubmed-10527306 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105273062023-09-28 Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor Alejandro-Sanjines, Ulbio Maisincho-Jivaja, Anthony Asanza, Victor Lorente-Leyva, Leandro L. Peluffo-Ordóñez, Diego H. Biomimetics (Basel) Article Automated industrial processes require a controller to obtain an output signal similar to the reference indicated by the user. There are controllers such as PIDs, which are efficient if the system does not change its initial conditions. However, if this is not the case, the controller must be retuned, affecting production times. In this work, an adaptive PID controller is developed for a DC motor speed plant using an artificial intelligence algorithm based on reinforcement learning. This algorithm uses an actor–critic agent, where its objective is to optimize the actor’s policy and train a critic for rewards. This will generate the appropriate gains without the need to know the system. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) was used, with a network composed of 300 neurons for the agent’s learning. Finally, the performance of the obtained controller is compared with a classical control one using a cost function. MDPI 2023-09-19 /pmc/articles/PMC10527306/ /pubmed/37754185 http://dx.doi.org/10.3390/biomimetics8050434 Text en © 2023 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 Alejandro-Sanjines, Ulbio Maisincho-Jivaja, Anthony Asanza, Victor Lorente-Leyva, Leandro L. Peluffo-Ordóñez, Diego H. Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor |
title | Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor |
title_full | Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor |
title_fullStr | Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor |
title_full_unstemmed | Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor |
title_short | Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor |
title_sort | adaptive pi controller based on a reinforcement learning algorithm for speed control of a dc motor |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10527306/ https://www.ncbi.nlm.nih.gov/pubmed/37754185 http://dx.doi.org/10.3390/biomimetics8050434 |
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