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An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems
This study focuses on the full-form model-free adaptive controller (FFMFAC) for SISO discrete-time nonlinear systems, and proposes enhanced FFMFAC. The proposed technique design incorporates long short-term memory neural networks (LSTMs) and fuzzy neural networks (FNNs). To be more precise, LSTMs ar...
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/PMC8871481/ https://www.ncbi.nlm.nih.gov/pubmed/35205458 http://dx.doi.org/10.3390/e24020163 |
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author | Yang, Ye Chen, Chen Lu, Jiangang |
author_facet | Yang, Ye Chen, Chen Lu, Jiangang |
author_sort | Yang, Ye |
collection | PubMed |
description | This study focuses on the full-form model-free adaptive controller (FFMFAC) for SISO discrete-time nonlinear systems, and proposes enhanced FFMFAC. The proposed technique design incorporates long short-term memory neural networks (LSTMs) and fuzzy neural networks (FNNs). To be more precise, LSTMs are utilized to adjust vital parameters of the FFMFAC online. Additionally, due to the high nonlinear approximation capabilities of FNNs, pseudo gradient (PG) values of the controller are estimated online. EFFMFAC is characterized by utilizing the measured I/O data for the online training of all introduced neural networks and does not involve offline training and specific models of the controlled system. Finally, the rationality and superiority are verified by two simulations and a supporting ablation analysis. Five individual performance indices are given, and the experimental findings show that EFFMFAC outperforms all other methods. Especially compared with the FFMFAC, EFFMFAC reduces the [Formula: see text] by 21.69% and 11.21%, respectively, proving it to be applicable for SISO discrete-time nonlinear systems. |
format | Online Article Text |
id | pubmed-8871481 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88714812022-02-25 An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems Yang, Ye Chen, Chen Lu, Jiangang Entropy (Basel) Article This study focuses on the full-form model-free adaptive controller (FFMFAC) for SISO discrete-time nonlinear systems, and proposes enhanced FFMFAC. The proposed technique design incorporates long short-term memory neural networks (LSTMs) and fuzzy neural networks (FNNs). To be more precise, LSTMs are utilized to adjust vital parameters of the FFMFAC online. Additionally, due to the high nonlinear approximation capabilities of FNNs, pseudo gradient (PG) values of the controller are estimated online. EFFMFAC is characterized by utilizing the measured I/O data for the online training of all introduced neural networks and does not involve offline training and specific models of the controlled system. Finally, the rationality and superiority are verified by two simulations and a supporting ablation analysis. Five individual performance indices are given, and the experimental findings show that EFFMFAC outperforms all other methods. Especially compared with the FFMFAC, EFFMFAC reduces the [Formula: see text] by 21.69% and 11.21%, respectively, proving it to be applicable for SISO discrete-time nonlinear systems. MDPI 2022-01-21 /pmc/articles/PMC8871481/ /pubmed/35205458 http://dx.doi.org/10.3390/e24020163 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 Yang, Ye Chen, Chen Lu, Jiangang An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems |
title | An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems |
title_full | An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems |
title_fullStr | An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems |
title_full_unstemmed | An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems |
title_short | An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems |
title_sort | enhanced full-form model-free adaptive controller for siso discrete-time nonlinear systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871481/ https://www.ncbi.nlm.nih.gov/pubmed/35205458 http://dx.doi.org/10.3390/e24020163 |
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