Evolutionary design for robust self-adaptive control
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da USP |
Texto Completo: | https://www.teses.usp.br/teses/disponiveis/18/18153/tde-08102024-161622/ |
Resumo: | Dealing with parametric uncertainties in mobile autonomous systems is a critical challenge. The difficulties scale when self-driving systems are operating in unconfined spaces or with interaction with people and other autonomous agents. Robust controllers have emerged as efficient solutions for ensuring autonomous navigation in such scenarios. However, uncertainty matrices for these agents are typically established using algebraic methods, necessitating prior knowledge of system dynamics. Consequently, control system designers rely heavily on the accuracy of uncertain models to achieve optimal control performance. To address these limitations, this study proposes a robust recursive controller developed through evolutionary optimization. A self-adapted algorithm was designed to incorporate robust control in single and multiobjective scenarios. Additionally, a local search strategy for addressing multiobjective optimization challenges is introduced. This methodology can be applied to any established multiobjective evolutionary algorithm found in existing literature. The findings demonstrate that this combination of a modelbased controller and machine learning significantly enhances system effectiveness in terms of robustness, stability, and smoothness. Moreover, this approach offers a more adaptable and comprehensive solution for scenarios with uncertainties in vision-based control and for heavy vehicles with significant mass variation. |
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Evolutionary design for robust self-adaptive controlDesign evolutivo para controle robusto auto adaptativoalgoritmos evolutivosautonomous systemscontrole robustoevolutionary algorithmsotimização uni e multiobjetivorobust controlsingle and multiobjective optimizationsistemas autônomosDealing with parametric uncertainties in mobile autonomous systems is a critical challenge. The difficulties scale when self-driving systems are operating in unconfined spaces or with interaction with people and other autonomous agents. Robust controllers have emerged as efficient solutions for ensuring autonomous navigation in such scenarios. However, uncertainty matrices for these agents are typically established using algebraic methods, necessitating prior knowledge of system dynamics. Consequently, control system designers rely heavily on the accuracy of uncertain models to achieve optimal control performance. To address these limitations, this study proposes a robust recursive controller developed through evolutionary optimization. A self-adapted algorithm was designed to incorporate robust control in single and multiobjective scenarios. Additionally, a local search strategy for addressing multiobjective optimization challenges is introduced. This methodology can be applied to any established multiobjective evolutionary algorithm found in existing literature. The findings demonstrate that this combination of a modelbased controller and machine learning significantly enhances system effectiveness in terms of robustness, stability, and smoothness. Moreover, this approach offers a more adaptable and comprehensive solution for scenarios with uncertainties in vision-based control and for heavy vehicles with significant mass variation.Lidar com incertezas paramétricas em sistemas autônomos móveis é um desafio crucial. As dificuldades aumentam quando os sistemas de condução autônoma operam em espaços não confinados ou em interação com pessoas e outros agentes. Os controladores robustos surgiram como soluções eficientes para garantir a navegação autônoma em tais cenários. No entanto, as matrizes de incerteza para estes agentes são normalmente estabelecidas utilizando métodos algébricos, o que exige um conhecimento prévio da dinâmica do sistema. Consequentemente, os projetistas de sistemas de controle dependem fortemente da precisão dos modelos de incertezas para obter um desempenho ótimo. Para resolver estas limitações, este estudo propõe um controlador recursivo robusto desenvolvido usando algoritmos de otimização evolutiva. Foi desenvolvido um algoritmo auto-adaptado que incorpora o controle robusto em cenários de objetivo único e multiobjetivo. Ademais, foi introduzida uma estratégia de busca local para lidar com os desafios de otimização multiobjetivo. Esta metodologia pode ser aplicada a qualquer algoritmo evolutivo multiobjetivo existente na literatura. Os resultados demonstram que esta combinação de controle baseado em modelos matemáticos e aprendizagem de máquinas melhora significativamente a eficácia do sistema em termos de robustez, estabilidade e suavidade. Além disso, esta abordagem oferece uma solução mais adaptável e abrangente para cenários com incertezas em controladores referenciados em visão e em veículos pesados com variação significativa de massa.Biblioteca Digitais de Teses e Dissertações da USPTerra, Marco HenriqueMorais, Gustavo Alves Prudencio de2024-08-30info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfhttps://www.teses.usp.br/teses/disponiveis/18/18153/tde-08102024-161622/reponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USPLiberar o conteúdo para acesso público.info:eu-repo/semantics/openAccesseng2024-10-09T19:32:02Zoai:teses.usp.br:tde-08102024-161622Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212024-10-09T19:32:02Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Evolutionary design for robust self-adaptive control Design evolutivo para controle robusto auto adaptativo |
title |
Evolutionary design for robust self-adaptive control |
spellingShingle |
Evolutionary design for robust self-adaptive control Morais, Gustavo Alves Prudencio de algoritmos evolutivos autonomous systems controle robusto evolutionary algorithms otimização uni e multiobjetivo robust control single and multiobjective optimization sistemas autônomos |
title_short |
Evolutionary design for robust self-adaptive control |
title_full |
Evolutionary design for robust self-adaptive control |
title_fullStr |
Evolutionary design for robust self-adaptive control |
title_full_unstemmed |
Evolutionary design for robust self-adaptive control |
title_sort |
Evolutionary design for robust self-adaptive control |
author |
Morais, Gustavo Alves Prudencio de |
author_facet |
Morais, Gustavo Alves Prudencio de |
author_role |
author |
dc.contributor.none.fl_str_mv |
Terra, Marco Henrique |
dc.contributor.author.fl_str_mv |
Morais, Gustavo Alves Prudencio de |
dc.subject.por.fl_str_mv |
algoritmos evolutivos autonomous systems controle robusto evolutionary algorithms otimização uni e multiobjetivo robust control single and multiobjective optimization sistemas autônomos |
topic |
algoritmos evolutivos autonomous systems controle robusto evolutionary algorithms otimização uni e multiobjetivo robust control single and multiobjective optimization sistemas autônomos |
description |
Dealing with parametric uncertainties in mobile autonomous systems is a critical challenge. The difficulties scale when self-driving systems are operating in unconfined spaces or with interaction with people and other autonomous agents. Robust controllers have emerged as efficient solutions for ensuring autonomous navigation in such scenarios. However, uncertainty matrices for these agents are typically established using algebraic methods, necessitating prior knowledge of system dynamics. Consequently, control system designers rely heavily on the accuracy of uncertain models to achieve optimal control performance. To address these limitations, this study proposes a robust recursive controller developed through evolutionary optimization. A self-adapted algorithm was designed to incorporate robust control in single and multiobjective scenarios. Additionally, a local search strategy for addressing multiobjective optimization challenges is introduced. This methodology can be applied to any established multiobjective evolutionary algorithm found in existing literature. The findings demonstrate that this combination of a modelbased controller and machine learning significantly enhances system effectiveness in terms of robustness, stability, and smoothness. Moreover, this approach offers a more adaptable and comprehensive solution for scenarios with uncertainties in vision-based control and for heavy vehicles with significant mass variation. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-08-30 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.teses.usp.br/teses/disponiveis/18/18153/tde-08102024-161622/ |
url |
https://www.teses.usp.br/teses/disponiveis/18/18153/tde-08102024-161622/ |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
|
dc.rights.driver.fl_str_mv |
Liberar o conteúdo para acesso público. info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Liberar o conteúdo para acesso público. |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.coverage.none.fl_str_mv |
|
dc.publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da USP instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Biblioteca Digital de Teses e Dissertações da USP |
collection |
Biblioteca Digital de Teses e Dissertações da USP |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br |
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1818279068707061760 |