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Multivariable Coupled System Control Method Based on Deep Reinforcement Learning

Due to the multi-loop coupling characteristics of multivariable systems, it is difficult for traditional control methods to achieve precise control effects. Therefore, this paper proposes a control method based on deep reinforcement learning to achieve stable and accurate control of multivariable co...

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Detalles Bibliográficos
Autores principales: Xu, Jin, Li, Han, Zhang, Qingxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10647332/
https://www.ncbi.nlm.nih.gov/pubmed/37960378
http://dx.doi.org/10.3390/s23218679
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author Xu, Jin
Li, Han
Zhang, Qingxin
author_facet Xu, Jin
Li, Han
Zhang, Qingxin
author_sort Xu, Jin
collection PubMed
description Due to the multi-loop coupling characteristics of multivariable systems, it is difficult for traditional control methods to achieve precise control effects. Therefore, this paper proposes a control method based on deep reinforcement learning to achieve stable and accurate control of multivariable coupling systems. Based on the proximal policy optimization algorithm (PPO), this method selects tanh as the activation function and normalizes the advantage function. At the same time, based on the characteristics of the multivariable coupling system, the reward function and controller are redesigned structures, achieving stable and precise control of the controlled system. In addition, this study used the amplitude of the control quantity output by the controller as an indicator to evaluate the controller’s performance. Finally, simulation verification was conducted in MATLAB/Simulink. The experimental results show that compared with decentralized control, decoupled control and traditional PPO control, the method proposed in this article achieves better control effects.
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spelling pubmed-106473322023-10-24 Multivariable Coupled System Control Method Based on Deep Reinforcement Learning Xu, Jin Li, Han Zhang, Qingxin Sensors (Basel) Article Due to the multi-loop coupling characteristics of multivariable systems, it is difficult for traditional control methods to achieve precise control effects. Therefore, this paper proposes a control method based on deep reinforcement learning to achieve stable and accurate control of multivariable coupling systems. Based on the proximal policy optimization algorithm (PPO), this method selects tanh as the activation function and normalizes the advantage function. At the same time, based on the characteristics of the multivariable coupling system, the reward function and controller are redesigned structures, achieving stable and precise control of the controlled system. In addition, this study used the amplitude of the control quantity output by the controller as an indicator to evaluate the controller’s performance. Finally, simulation verification was conducted in MATLAB/Simulink. The experimental results show that compared with decentralized control, decoupled control and traditional PPO control, the method proposed in this article achieves better control effects. MDPI 2023-10-24 /pmc/articles/PMC10647332/ /pubmed/37960378 http://dx.doi.org/10.3390/s23218679 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
Xu, Jin
Li, Han
Zhang, Qingxin
Multivariable Coupled System Control Method Based on Deep Reinforcement Learning
title Multivariable Coupled System Control Method Based on Deep Reinforcement Learning
title_full Multivariable Coupled System Control Method Based on Deep Reinforcement Learning
title_fullStr Multivariable Coupled System Control Method Based on Deep Reinforcement Learning
title_full_unstemmed Multivariable Coupled System Control Method Based on Deep Reinforcement Learning
title_short Multivariable Coupled System Control Method Based on Deep Reinforcement Learning
title_sort multivariable coupled system control method based on deep reinforcement learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10647332/
https://www.ncbi.nlm.nih.gov/pubmed/37960378
http://dx.doi.org/10.3390/s23218679
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