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Modeling and Trajectory Tracking Model Predictive Control Novel Method of AUV Based on CFD Data
In this paper, a novel model predictive control (MPC) method based on the population normal probability division genetic algorithm and ant colony optimization (GA-ACO) method is proposed to optimally solve the problem of standard MPC with constraints that generally cannot yield global optimal soluti...
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/PMC9185449/ https://www.ncbi.nlm.nih.gov/pubmed/35684855 http://dx.doi.org/10.3390/s22114234 |
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author | Bao, Han Zhu, Haitao |
author_facet | Bao, Han Zhu, Haitao |
author_sort | Bao, Han |
collection | PubMed |
description | In this paper, a novel model predictive control (MPC) method based on the population normal probability division genetic algorithm and ant colony optimization (GA-ACO) method is proposed to optimally solve the problem of standard MPC with constraints that generally cannot yield global optimal solutions when using quadratic programming (QP). Combined with dynamic sliding mode control (SMC), this model is applied to the dynamic trajectory tracking control of autonomous underwater vehicles (AUVs). First, the computational fluid dynamics (CFD) simulation platform ANSYS Fluent is used to solve for the main hydrodynamic coefficients required to establish the AUV dynamic model. Then, the novel model predictive controller is used to obtain the desired velocity command of the AUV. To reduce the influence of external interference and realize accurate velocity tracking, dynamic SMC is used to obtain the control input command. In addition, stability analysis based on the Lyapunov method proves the asymptotic stability of the controller. Finally, the trajectory tracking performance of the AUV in an underwater, three-dimensional environment is verified by using the MATLAB/Simulink simulation platform. The results verify the effectiveness and robustness of the proposed control method. |
format | Online Article Text |
id | pubmed-9185449 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91854492022-06-11 Modeling and Trajectory Tracking Model Predictive Control Novel Method of AUV Based on CFD Data Bao, Han Zhu, Haitao Sensors (Basel) Article In this paper, a novel model predictive control (MPC) method based on the population normal probability division genetic algorithm and ant colony optimization (GA-ACO) method is proposed to optimally solve the problem of standard MPC with constraints that generally cannot yield global optimal solutions when using quadratic programming (QP). Combined with dynamic sliding mode control (SMC), this model is applied to the dynamic trajectory tracking control of autonomous underwater vehicles (AUVs). First, the computational fluid dynamics (CFD) simulation platform ANSYS Fluent is used to solve for the main hydrodynamic coefficients required to establish the AUV dynamic model. Then, the novel model predictive controller is used to obtain the desired velocity command of the AUV. To reduce the influence of external interference and realize accurate velocity tracking, dynamic SMC is used to obtain the control input command. In addition, stability analysis based on the Lyapunov method proves the asymptotic stability of the controller. Finally, the trajectory tracking performance of the AUV in an underwater, three-dimensional environment is verified by using the MATLAB/Simulink simulation platform. The results verify the effectiveness and robustness of the proposed control method. MDPI 2022-06-01 /pmc/articles/PMC9185449/ /pubmed/35684855 http://dx.doi.org/10.3390/s22114234 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 Bao, Han Zhu, Haitao Modeling and Trajectory Tracking Model Predictive Control Novel Method of AUV Based on CFD Data |
title | Modeling and Trajectory Tracking Model Predictive Control Novel Method of AUV Based on CFD Data |
title_full | Modeling and Trajectory Tracking Model Predictive Control Novel Method of AUV Based on CFD Data |
title_fullStr | Modeling and Trajectory Tracking Model Predictive Control Novel Method of AUV Based on CFD Data |
title_full_unstemmed | Modeling and Trajectory Tracking Model Predictive Control Novel Method of AUV Based on CFD Data |
title_short | Modeling and Trajectory Tracking Model Predictive Control Novel Method of AUV Based on CFD Data |
title_sort | modeling and trajectory tracking model predictive control novel method of auv based on cfd data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185449/ https://www.ncbi.nlm.nih.gov/pubmed/35684855 http://dx.doi.org/10.3390/s22114234 |
work_keys_str_mv | AT baohan modelingandtrajectorytrackingmodelpredictivecontrolnovelmethodofauvbasedoncfddata AT zhuhaitao modelingandtrajectorytrackingmodelpredictivecontrolnovelmethodofauvbasedoncfddata |