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The Adaptive Dynamic Programming Toolbox

The paper develops the adaptive dynamic programming toolbox (ADPT), which is a MATLAB-based software package and computationally solves optimal control problems for continuous-time control-affine systems. The ADPT produces approximate optimal feedback controls by employing the adaptive dynamic progr...

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Detalles Bibliográficos
Autores principales: Xing, Xiaowei, Chang, Dong Eui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8402451/
https://www.ncbi.nlm.nih.gov/pubmed/34451052
http://dx.doi.org/10.3390/s21165609
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author Xing, Xiaowei
Chang, Dong Eui
author_facet Xing, Xiaowei
Chang, Dong Eui
author_sort Xing, Xiaowei
collection PubMed
description The paper develops the adaptive dynamic programming toolbox (ADPT), which is a MATLAB-based software package and computationally solves optimal control problems for continuous-time control-affine systems. The ADPT produces approximate optimal feedback controls by employing the adaptive dynamic programming technique and solving the Hamilton–Jacobi–Bellman equation approximately. A novel implementation method is derived to optimize the memory consumption by the ADPT throughout its execution. The ADPT supports two working modes: model-based mode and model-free mode. In the former mode, the ADPT computes optimal feedback controls provided the system dynamics. In the latter mode, optimal feedback controls are generated from the measurements of system trajectories, without the requirement of knowledge of the system model. Multiple setting options are provided in the ADPT, such that various customized circumstances can be accommodated. Compared to other popular software toolboxes for optimal control, the ADPT features computational precision and time efficiency, which is illustrated with its applications to a highly non-linear satellite attitude control problem.
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spelling pubmed-84024512021-08-29 The Adaptive Dynamic Programming Toolbox Xing, Xiaowei Chang, Dong Eui Sensors (Basel) Article The paper develops the adaptive dynamic programming toolbox (ADPT), which is a MATLAB-based software package and computationally solves optimal control problems for continuous-time control-affine systems. The ADPT produces approximate optimal feedback controls by employing the adaptive dynamic programming technique and solving the Hamilton–Jacobi–Bellman equation approximately. A novel implementation method is derived to optimize the memory consumption by the ADPT throughout its execution. The ADPT supports two working modes: model-based mode and model-free mode. In the former mode, the ADPT computes optimal feedback controls provided the system dynamics. In the latter mode, optimal feedback controls are generated from the measurements of system trajectories, without the requirement of knowledge of the system model. Multiple setting options are provided in the ADPT, such that various customized circumstances can be accommodated. Compared to other popular software toolboxes for optimal control, the ADPT features computational precision and time efficiency, which is illustrated with its applications to a highly non-linear satellite attitude control problem. MDPI 2021-08-20 /pmc/articles/PMC8402451/ /pubmed/34451052 http://dx.doi.org/10.3390/s21165609 Text en © 2021 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
Xing, Xiaowei
Chang, Dong Eui
The Adaptive Dynamic Programming Toolbox
title The Adaptive Dynamic Programming Toolbox
title_full The Adaptive Dynamic Programming Toolbox
title_fullStr The Adaptive Dynamic Programming Toolbox
title_full_unstemmed The Adaptive Dynamic Programming Toolbox
title_short The Adaptive Dynamic Programming Toolbox
title_sort adaptive dynamic programming toolbox
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8402451/
https://www.ncbi.nlm.nih.gov/pubmed/34451052
http://dx.doi.org/10.3390/s21165609
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