PSO-Based Algorithm Applied to Quadcopter Micro Air Vehicle Controller Design

Due to the rapid development of science and technology in recent times, many effective controllers are designed and applied successfully to complicated systems. The significant task of controller design is to determine optimized control gains in a short period of time. With this purpose in mind, a c...

Descripción completa

Detalles Bibliográficos
Autores principales: Tran, Huu-Khoa, Chiou, Juing-Shian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6190017/
https://www.ncbi.nlm.nih.gov/pubmed/30404342
http://dx.doi.org/10.3390/mi7090168
_version_ 1783363480180490240
author Tran, Huu-Khoa
Chiou, Juing-Shian
author_facet Tran, Huu-Khoa
Chiou, Juing-Shian
author_sort Tran, Huu-Khoa
collection PubMed
description Due to the rapid development of science and technology in recent times, many effective controllers are designed and applied successfully to complicated systems. The significant task of controller design is to determine optimized control gains in a short period of time. With this purpose in mind, a combination of the particle swarm optimization (PSO)-based algorithm and the evolutionary programming (EP) algorithm is introduced in this article. The benefit of this integration algorithm is the creation of new best-parameters for control design schemes. The proposed controller designs are then demonstrated to have the best performance for nonlinear micro air vehicle models.
format Online
Article
Text
id pubmed-6190017
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-61900172018-11-01 PSO-Based Algorithm Applied to Quadcopter Micro Air Vehicle Controller Design Tran, Huu-Khoa Chiou, Juing-Shian Micromachines (Basel) Article Due to the rapid development of science and technology in recent times, many effective controllers are designed and applied successfully to complicated systems. The significant task of controller design is to determine optimized control gains in a short period of time. With this purpose in mind, a combination of the particle swarm optimization (PSO)-based algorithm and the evolutionary programming (EP) algorithm is introduced in this article. The benefit of this integration algorithm is the creation of new best-parameters for control design schemes. The proposed controller designs are then demonstrated to have the best performance for nonlinear micro air vehicle models. MDPI 2016-09-15 /pmc/articles/PMC6190017/ /pubmed/30404342 http://dx.doi.org/10.3390/mi7090168 Text en © 2016 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tran, Huu-Khoa
Chiou, Juing-Shian
PSO-Based Algorithm Applied to Quadcopter Micro Air Vehicle Controller Design
title PSO-Based Algorithm Applied to Quadcopter Micro Air Vehicle Controller Design
title_full PSO-Based Algorithm Applied to Quadcopter Micro Air Vehicle Controller Design
title_fullStr PSO-Based Algorithm Applied to Quadcopter Micro Air Vehicle Controller Design
title_full_unstemmed PSO-Based Algorithm Applied to Quadcopter Micro Air Vehicle Controller Design
title_short PSO-Based Algorithm Applied to Quadcopter Micro Air Vehicle Controller Design
title_sort pso-based algorithm applied to quadcopter micro air vehicle controller design
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6190017/
https://www.ncbi.nlm.nih.gov/pubmed/30404342
http://dx.doi.org/10.3390/mi7090168
work_keys_str_mv AT tranhuukhoa psobasedalgorithmappliedtoquadcoptermicroairvehiclecontrollerdesign
AT chioujuingshian psobasedalgorithmappliedtoquadcoptermicroairvehiclecontrollerdesign

Ejemplares similares