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Bio-inspired optimization algorithms applied to the GAPID control of a Buck converter

The objective of this work is to realize a comparative study of which bio-inspired metaheuristic strategies are the best to be used when finding the optimal gains for a Gaussian Adaptive PID control system applied to a Buck Converter. This comes from two necessities, the first one is that to surpass...

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Autor principal: Itaborahy Filho, Marco Antonio
Formato: Dissertação
Idioma: Inglês
Publicado em: Universidade Tecnológica Federal do Paraná 2021
Assuntos:
Controladores PID
Algorítmos genéticos
Heurística
PID controllers
Genetic algorithms
Heuristic
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
Engenharia/Tecnologia/Gestão
Acesso em linha: http://repositorio.utfpr.edu.br/jspui/handle/1/25060
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spelling riut-1-250602021-05-28T06:11:54Z Bio-inspired optimization algorithms applied to the GAPID control of a Buck converter Algoritmos de otimização bio-inspirados aplicados ao controle de um conversor Buck Itaborahy Filho, Marco Antonio Kaster, Mauricio dos Santos https://orcid.org/0000-0002-7687-1297 http://lattes.cnpq.br/5494434934031784 Siqueira, Hugo Valadares https://orcid.org/0000-0002-1278-4602 http://lattes.cnpq.br/6904980376005290 Kaster, Mauricio dos Santos https://orcid.org/0000-0002-7687-1297 http://lattes.cnpq.br/5494434934031784 Trojan, Flavio https://orcid.org/0000-0003-2274-5321 http://lattes.cnpq.br/1688457940211697 Martins, Marcella Scoczynski Ribeiro https://orcid.org/0000-0002-5716-4968 http://lattes.cnpq.br/5212122361603572 Parpinelli, Rafael Stubs https://orcid.org/0000-0001-7326-5032 http://lattes.cnpq.br/4456007001373501 Controladores PID Algorítmos genéticos Heurística PID controllers Genetic algorithms Heuristic CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA Engenharia/Tecnologia/Gestão The objective of this work is to realize a comparative study of which bio-inspired metaheuristic strategies are the best to be used when finding the optimal gains for a Gaussian Adaptive PID control system applied to a Buck Converter. This comes from two necessities, the first one is that to surpass the performance of linear control systems a more advanced strategy such as adaptive control is required; the second is that since there are no deterministic optimization methods for such strategies, the use of metaheuristics is deemed necessary, and according to the No Free Lunch theorem such comparative studies ought to be made to understand what optimization techniques ares better suitable for such a problem. The chosen techniques were the Genetic Algorithm, the Particle Swarm Optimization and the Differential Evolution. The Gaussian function is employed as the adaptive rule because it is a smooth curve with smooth derivatives that avoids the potential of chattering and abrupt control changes. After evaluating the responses found from simulating all variations of each of the optimization strategies we were able to create an analytical and statistical comparison of each technique and show how an adaptive control system can create a faster and more robust response when compared to the linear PID. O objetivo deste trabalho é criar um estudo comparativo de quais estratégias metaheurísticas bioinspiradas podem ser as melhores para otimizar os ganhos ótimos de um sistema de controle PID Adaptativo Gaussiano para um Conversor Buck. Isso vem de duas necessidades, a primeira é que para superar o desempenho dos sistemas de controle linear é necessária uma estratégia mais avançada como o controle adaptativo; a segunda é que como não existem métodos de otimização determinísticos para tais estratégias, o uso de metaheurísticas é considerado necessário, e de acordo com o teorema do No Free Lunch tais estudos comparativos devem ser feitos para entender quais técnicas de otimização são mais adequadas para tal problema. As técnicas escolhidas foram o Algoritmo Genético, a Otimização do Enxame de Partículas e a Evolução Diferencial. O Gaussian Adaptive PID (GAPID) foi escolhido para o controle adaptativo por ser ser baseado na função Gaussiana, que é uma curva suave com derivadas suaves que evita vibrações e mudanças bruscas no controle. Depois de avaliar as respostas encontradas na simulação de todas as variações de cada uma das estratégias de otimização, foi criada uma comparação analítica e estatística de cada técnica mostrando como um sistema de controle adaptativo pode gerar uma resposta mais rápida e robusta quando comparado ao PID linear. 2021-05-27T21:17:44Z 2021-05-27T21:17:44Z 2021-03-12 masterThesis ITABORAHY FILHO, Marco Antonio. Bio-inspired optimization algorithms applied to the GAPID control of a Buck converter. 2021. Dissertação (Mestrado em Engenharia Elétrica) - Universidade Tecnológica Federal do Paraná, Ponta Grossa, 2021. http://repositorio.utfpr.edu.br/jspui/handle/1/25060 eng openAccess Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Universidade Tecnológica Federal do Paraná Ponta Grossa Brasil Programa de Pós-Graduação em Engenharia Elétrica UTFPR
institution Universidade Tecnológica Federal do Paraná
collection RIUT
language Inglês
topic Controladores PID
Algorítmos genéticos
Heurística
PID controllers
Genetic algorithms
Heuristic
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
Engenharia/Tecnologia/Gestão
spellingShingle Controladores PID
Algorítmos genéticos
Heurística
PID controllers
Genetic algorithms
Heuristic
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
Engenharia/Tecnologia/Gestão
Itaborahy Filho, Marco Antonio
Bio-inspired optimization algorithms applied to the GAPID control of a Buck converter
description The objective of this work is to realize a comparative study of which bio-inspired metaheuristic strategies are the best to be used when finding the optimal gains for a Gaussian Adaptive PID control system applied to a Buck Converter. This comes from two necessities, the first one is that to surpass the performance of linear control systems a more advanced strategy such as adaptive control is required; the second is that since there are no deterministic optimization methods for such strategies, the use of metaheuristics is deemed necessary, and according to the No Free Lunch theorem such comparative studies ought to be made to understand what optimization techniques ares better suitable for such a problem. The chosen techniques were the Genetic Algorithm, the Particle Swarm Optimization and the Differential Evolution. The Gaussian function is employed as the adaptive rule because it is a smooth curve with smooth derivatives that avoids the potential of chattering and abrupt control changes. After evaluating the responses found from simulating all variations of each of the optimization strategies we were able to create an analytical and statistical comparison of each technique and show how an adaptive control system can create a faster and more robust response when compared to the linear PID.
format Dissertação
author Itaborahy Filho, Marco Antonio
author_sort Itaborahy Filho, Marco Antonio
title Bio-inspired optimization algorithms applied to the GAPID control of a Buck converter
title_short Bio-inspired optimization algorithms applied to the GAPID control of a Buck converter
title_full Bio-inspired optimization algorithms applied to the GAPID control of a Buck converter
title_fullStr Bio-inspired optimization algorithms applied to the GAPID control of a Buck converter
title_full_unstemmed Bio-inspired optimization algorithms applied to the GAPID control of a Buck converter
title_sort bio-inspired optimization algorithms applied to the gapid control of a buck converter
publisher Universidade Tecnológica Federal do Paraná
publishDate 2021
citation ITABORAHY FILHO, Marco Antonio. Bio-inspired optimization algorithms applied to the GAPID control of a Buck converter. 2021. Dissertação (Mestrado em Engenharia Elétrica) - Universidade Tecnológica Federal do Paraná, Ponta Grossa, 2021.
url http://repositorio.utfpr.edu.br/jspui/handle/1/25060
_version_ 1805318736125624320
score 10,814766

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