Optimal fuzzy PID control tuned with genetic algorithms

Detalhes bibliográficos
Autor(a) principal: Santos, Carlos Miguel Almeida
Data de Publicação: 2013
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/4628
Resumo: Fuzzy logic controllers (FLC) are intelligent systems, based on heuristic knowledge, that have been largely applied in numerous areas of everyday life. They can be used to describe a linear or nonlinear system and are suitable when a real system is not known or too difficult to find their model. FLC provide a formal methodology for representing, manipulating and implementing a human heuristic knowledge on how to control a system. These controllers can be seen as artificial decision makers that operate in a closed-loop system, in real time. The main aim of this work was to develop a single optimal fuzzy controller, easily adaptable to a wide range of systems – simple to complex, linear to nonlinear – and able to control all these systems. Due to their efficiency in searching and finding optimal solution for high complexity problems, GAs were used to perform the FLC tuning by finding the best parameters to obtain the best responses. The work was performed using the MATLAB/SIMULINK software. This is a very useful tool that provides an easy way to test and analyse the FLC, the PID and the GAs in the same environment. Therefore, it was proposed a Fuzzy PID controller (FL-PID) type namely, the Fuzzy PD+I. For that, the controller was compared with the classical PID controller tuned with, the heuristic Ziegler-Nichols tuning method, the optimal Zhuang-Atherton tuning method and the GA method itself. The IAE, ISE, ITAE and ITSE criteria, used as the GA fitness functions, were applied to compare the controllers performance used in this work. Overall, and for most systems, the FL-PID results tuned with GAs were very satisfactory. Moreover, in some cases the results were substantially better than for the other PID controllers. The best system responses were obtained with the IAE and ITAE criteria used to tune the FL-PID and PID controllers.
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spelling Optimal fuzzy PID control tuned with genetic algorithmsFuzzyIntelligent systemsHeuristicControlOptimalGAPIDLógica difusaSistemas inteligentesHeurísticaControloÓtimoAGFuzzy logic controllers (FLC) are intelligent systems, based on heuristic knowledge, that have been largely applied in numerous areas of everyday life. They can be used to describe a linear or nonlinear system and are suitable when a real system is not known or too difficult to find their model. FLC provide a formal methodology for representing, manipulating and implementing a human heuristic knowledge on how to control a system. These controllers can be seen as artificial decision makers that operate in a closed-loop system, in real time. The main aim of this work was to develop a single optimal fuzzy controller, easily adaptable to a wide range of systems – simple to complex, linear to nonlinear – and able to control all these systems. Due to their efficiency in searching and finding optimal solution for high complexity problems, GAs were used to perform the FLC tuning by finding the best parameters to obtain the best responses. The work was performed using the MATLAB/SIMULINK software. This is a very useful tool that provides an easy way to test and analyse the FLC, the PID and the GAs in the same environment. Therefore, it was proposed a Fuzzy PID controller (FL-PID) type namely, the Fuzzy PD+I. For that, the controller was compared with the classical PID controller tuned with, the heuristic Ziegler-Nichols tuning method, the optimal Zhuang-Atherton tuning method and the GA method itself. The IAE, ISE, ITAE and ITSE criteria, used as the GA fitness functions, were applied to compare the controllers performance used in this work. Overall, and for most systems, the FL-PID results tuned with GAs were very satisfactory. Moreover, in some cases the results were substantially better than for the other PID controllers. The best system responses were obtained with the IAE and ITAE criteria used to tune the FL-PID and PID controllers.Os Controladores Lógicos Difusos (CLD) são sistemas inteligentes, baseados em conhecimentos heurísticos, que têm vindo a ser amplamente aplicados em inúmeras áreas do quotidiano. Podem ser usados para descrever sistemas lineares ou não-lineares, tornando-se apropriados quando se desconhece o modelo do sistema real ou no caso de o sistema ser difícil de modelar. Os CLD apresentam uma metodologia formal para representar, manipular e implementar um conhecimento heurístico de como controlar um sistema. Estes controladores funcionam como gestores artificiais de decisão que operam em sistemas de malha-fechada, e em tempo real. O principal objetivo deste trabalho consistiu no desenvolvimento de um único controlador difuso ótimo, facilmente adaptável a uma vasta gama de sistemas – simples a complexos, lineares a não-lineares – e com capacidade para controlar sistemas distintos. Devido à sua eficácia na procura e descoberta de soluções ótimas, para sistemas de elevada complexidade, os Algoritmos Genéticos (AG) foram usados para a sintonia do CLD através da procura dos melhores parâmetros por forma a encontrar as melhores respostas. O trabalho foi realizado usando o software MATLAB/SIMULINK. Esta é uma ferramenta útil que permite facilmente testar e analisar, no mesmo ambiente, o CLD, o PID e os AG. Por esta razão, foi proposto um controlador difuso do tipo PID, concretamente o Controlador Lógico Difuso PD+I (CLD-PID). Este controlador foi comparado com o controlador PID clássico sintonizado com o método heurístico de Ziegler-Nichols, o método ótimo de sintonização de Zhuang-Atherton e o próprio método de AG. Os critérios IAE, ISE, ITAE e ITSE, usados como as funções de avaliação dos AG, foram utilizados para comparar os desempenhos dos controladores usados neste trabalho. De um modo geral, e para a maioria dos sistemas, os resultados do CLD-PID sintonizados com os AG foram bastantes satisfatórios. Para além disso, em alguns casos, os resultados foram consideravelmente melhores do que para os restantes controladores PID. As melhores respostas de sistemas foram obtidas com os critérios IAE e ITAE, que foram usados para sintonizar os controladores CLD-PID e PID.Instituto Politécnico do Porto. Instituto Superior de Engenharia do Porto.Barbosa, Ramiro S.Repositório Científico do Instituto Politécnico do PortoSantos, Carlos Miguel Almeida2014-07-02T11:16:41Z20132013-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.22/4628TID:201813300enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T12:44:46Zoai:recipp.ipp.pt:10400.22/4628Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:25:31.146090Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Optimal fuzzy PID control tuned with genetic algorithms
title Optimal fuzzy PID control tuned with genetic algorithms
spellingShingle Optimal fuzzy PID control tuned with genetic algorithms
Santos, Carlos Miguel Almeida
Fuzzy
Intelligent systems
Heuristic
Control
Optimal
GA
PID
Lógica difusa
Sistemas inteligentes
Heurística
Controlo
Ótimo
AG
title_short Optimal fuzzy PID control tuned with genetic algorithms
title_full Optimal fuzzy PID control tuned with genetic algorithms
title_fullStr Optimal fuzzy PID control tuned with genetic algorithms
title_full_unstemmed Optimal fuzzy PID control tuned with genetic algorithms
title_sort Optimal fuzzy PID control tuned with genetic algorithms
author Santos, Carlos Miguel Almeida
author_facet Santos, Carlos Miguel Almeida
author_role author
dc.contributor.none.fl_str_mv Barbosa, Ramiro S.
Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Santos, Carlos Miguel Almeida
dc.subject.por.fl_str_mv Fuzzy
Intelligent systems
Heuristic
Control
Optimal
GA
PID
Lógica difusa
Sistemas inteligentes
Heurística
Controlo
Ótimo
AG
topic Fuzzy
Intelligent systems
Heuristic
Control
Optimal
GA
PID
Lógica difusa
Sistemas inteligentes
Heurística
Controlo
Ótimo
AG
description Fuzzy logic controllers (FLC) are intelligent systems, based on heuristic knowledge, that have been largely applied in numerous areas of everyday life. They can be used to describe a linear or nonlinear system and are suitable when a real system is not known or too difficult to find their model. FLC provide a formal methodology for representing, manipulating and implementing a human heuristic knowledge on how to control a system. These controllers can be seen as artificial decision makers that operate in a closed-loop system, in real time. The main aim of this work was to develop a single optimal fuzzy controller, easily adaptable to a wide range of systems – simple to complex, linear to nonlinear – and able to control all these systems. Due to their efficiency in searching and finding optimal solution for high complexity problems, GAs were used to perform the FLC tuning by finding the best parameters to obtain the best responses. The work was performed using the MATLAB/SIMULINK software. This is a very useful tool that provides an easy way to test and analyse the FLC, the PID and the GAs in the same environment. Therefore, it was proposed a Fuzzy PID controller (FL-PID) type namely, the Fuzzy PD+I. For that, the controller was compared with the classical PID controller tuned with, the heuristic Ziegler-Nichols tuning method, the optimal Zhuang-Atherton tuning method and the GA method itself. The IAE, ISE, ITAE and ITSE criteria, used as the GA fitness functions, were applied to compare the controllers performance used in this work. Overall, and for most systems, the FL-PID results tuned with GAs were very satisfactory. Moreover, in some cases the results were substantially better than for the other PID controllers. The best system responses were obtained with the IAE and ITAE criteria used to tune the FL-PID and PID controllers.
publishDate 2013
dc.date.none.fl_str_mv 2013
2013-01-01T00:00:00Z
2014-07-02T11:16:41Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/4628
TID:201813300
url http://hdl.handle.net/10400.22/4628
identifier_str_mv TID:201813300
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Instituto Politécnico do Porto. Instituto Superior de Engenharia do Porto.
publisher.none.fl_str_mv Instituto Politécnico do Porto. Instituto Superior de Engenharia do Porto.
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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