Controle preditivo robusto baseado em modelo aplicado a sistemas lineares com saltos Markovianos
| Ano de defesa: | 2019 |
|---|---|
| Autor(a) principal: | |
| Orientador(a): | |
| Banca de defesa: | |
| Tipo de documento: | Tese |
| Tipo de acesso: | Acesso aberto |
| Idioma: | por |
| Instituição de defesa: |
Universidade Federal de Minas Gerais
|
| Programa de Pós-Graduação: |
Não Informado pela instituição
|
| Departamento: |
Não Informado pela instituição
|
| País: |
Não Informado pela instituição
|
| Palavras-chave em Português: | |
| Link de acesso: | https://hdl.handle.net/1843/31636 |
Resumo: | In this thesis, robust model predictive control techniques applied to discrete-time Markov jump linear systems are introduced. Two control scenarios are addressed. In the first scenario, it is developed a control solution that minimizes the expected value of an infinite-horizon quadratic cost. As a byproduct, mean square stability is obtained under two cases: i) without constraints, and ii) with constraints on control input and state. The second control scenario considers the minimization of the expected value of quadratic finite-horizon cost. This scenario considers not only stochastic additive noise, but also constraints that are imposed on the second moment of both state and control. Numerical experiments illustrate the results for both scenarios. |
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2019-12-19T19:40:53Z2025-09-08T23:49:46Z2019-12-19T19:40:53Z2019-12-04https://hdl.handle.net/1843/31636In this thesis, robust model predictive control techniques applied to discrete-time Markov jump linear systems are introduced. Two control scenarios are addressed. In the first scenario, it is developed a control solution that minimizes the expected value of an infinite-horizon quadratic cost. As a byproduct, mean square stability is obtained under two cases: i) without constraints, and ii) with constraints on control input and state. The second control scenario considers the minimization of the expected value of quadratic finite-horizon cost. This scenario considers not only stochastic additive noise, but also constraints that are imposed on the second moment of both state and control. Numerical experiments illustrate the results for both scenarios.porUniversidade Federal de Minas GeraisSistemas lineares com saltos MarkovianosControle robustoControle preditivo baseado em modeloRuído aditivoDesigualdades matriciais linearesEngenharia elétricaSistemas linearesMarkov, Processos deControle robustoControle preditivoControle preditivo robusto baseado em modelo aplicado a sistemas lineares com saltos MarkovianosRobust model predictive control applied to Markov jump linear systemsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisRosileide de Oliveira Lopesinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGhttp://lattes.cnpq.br/3248976355757595Reinaldo Martinez Palhareshttp://lattes.cnpq.br/1268773789851994Eduardo Mazoni Andrade Marçal MendesLeonardo Antônio Borges TôrresJoão Bosco Ribeiro do ValRoberto Kawakami Harrop GalvãoLuciano Antônio Frezzatto SantosVíctor Costa da Silva CamposEsta tese apresenta técnicas de controle preditivo robusto baseado em modelo aplicadas a sistemas lineares com saltos Markovianos a tempo discreto. Dois cenários de controle são tratados. No primeiro cenário desenvolve-se uma solução de controle que minimiza o valor esperado de um custo quadrático de horizonte infinito. Como subproduto nesta etapa, obtém-se estabilidade em média quadrática para dois casos: i) sem restrições e, ii) com restrições rígidas na entrada de controle e no estado. No segundo cenário de controle, trata-se da minimização do valor esperado de um custo quadrático de horizonte finito. Neste caso, considera-se a presença de ruído aditivo estocástico e contempla-se também restrições que são impostas sobre o segundo momento do estado e da variável de controle. Para ambos os cenários são apresentados experimentos numéricos que ilustram as técnicas propostas.BrasilENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICAPrograma de Pós-Graduação em Engenharia ElétricaUFMGORIGINALTese_de_Doutorado_Rosileide_Lopes.pdfapplication/pdf1170827https://repositorio.ufmg.br//bitstreams/8503b70a-50fc-4420-969c-535a42870442/download47f12ba3eaafd185e9ad8439c465a0d2MD51trueAnonymousREADLICENSElicense.txttext/plain2119https://repositorio.ufmg.br//bitstreams/4d789034-a29d-49ae-8292-06c5453c0d43/download34badce4be7e31e3adb4575ae96af679MD52falseAnonymousREADTEXTTese_de_Doutorado_Rosileide_Lopes.pdf.txttext/plain130562https://repositorio.ufmg.br//bitstreams/a8131698-83f4-46be-912a-4cfec03e155b/downloadfee9f2c557b1b1baa3836a2d64cd5a75MD53falseAnonymousREAD1843/316362025-09-08 20:49:46.424open.accessoai:repositorio.ufmg.br:1843/31636https://repositorio.ufmg.br/Repositório InstitucionalPUBhttps://repositorio.ufmg.br/oairepositorio@ufmg.bropendoar:2025-09-08T23:49:46Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)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 |
| dc.title.none.fl_str_mv |
Controle preditivo robusto baseado em modelo aplicado a sistemas lineares com saltos Markovianos |
| dc.title.alternative.none.fl_str_mv |
Robust model predictive control applied to Markov jump linear systems |
| title |
Controle preditivo robusto baseado em modelo aplicado a sistemas lineares com saltos Markovianos |
| spellingShingle |
Controle preditivo robusto baseado em modelo aplicado a sistemas lineares com saltos Markovianos Rosileide de Oliveira Lopes Engenharia elétrica Sistemas lineares Markov, Processos de Controle robusto Controle preditivo Sistemas lineares com saltos Markovianos Controle robusto Controle preditivo baseado em modelo Ruído aditivo Desigualdades matriciais lineares |
| title_short |
Controle preditivo robusto baseado em modelo aplicado a sistemas lineares com saltos Markovianos |
| title_full |
Controle preditivo robusto baseado em modelo aplicado a sistemas lineares com saltos Markovianos |
| title_fullStr |
Controle preditivo robusto baseado em modelo aplicado a sistemas lineares com saltos Markovianos |
| title_full_unstemmed |
Controle preditivo robusto baseado em modelo aplicado a sistemas lineares com saltos Markovianos |
| title_sort |
Controle preditivo robusto baseado em modelo aplicado a sistemas lineares com saltos Markovianos |
| author |
Rosileide de Oliveira Lopes |
| author_facet |
Rosileide de Oliveira Lopes |
| author_role |
author |
| dc.contributor.author.fl_str_mv |
Rosileide de Oliveira Lopes |
| dc.subject.por.fl_str_mv |
Engenharia elétrica Sistemas lineares Markov, Processos de Controle robusto Controle preditivo |
| topic |
Engenharia elétrica Sistemas lineares Markov, Processos de Controle robusto Controle preditivo Sistemas lineares com saltos Markovianos Controle robusto Controle preditivo baseado em modelo Ruído aditivo Desigualdades matriciais lineares |
| dc.subject.other.none.fl_str_mv |
Sistemas lineares com saltos Markovianos Controle robusto Controle preditivo baseado em modelo Ruído aditivo Desigualdades matriciais lineares |
| description |
In this thesis, robust model predictive control techniques applied to discrete-time Markov jump linear systems are introduced. Two control scenarios are addressed. In the first scenario, it is developed a control solution that minimizes the expected value of an infinite-horizon quadratic cost. As a byproduct, mean square stability is obtained under two cases: i) without constraints, and ii) with constraints on control input and state. The second control scenario considers the minimization of the expected value of quadratic finite-horizon cost. This scenario considers not only stochastic additive noise, but also constraints that are imposed on the second moment of both state and control. Numerical experiments illustrate the results for both scenarios. |
| publishDate |
2019 |
| dc.date.accessioned.fl_str_mv |
2019-12-19T19:40:53Z 2025-09-08T23:49:46Z |
| dc.date.available.fl_str_mv |
2019-12-19T19:40:53Z |
| dc.date.issued.fl_str_mv |
2019-12-04 |
| 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://hdl.handle.net/1843/31636 |
| url |
https://hdl.handle.net/1843/31636 |
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por |
| language |
por |
| dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
| dc.publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
| publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
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reponame:Repositório Institucional da UFMG instname:Universidade Federal de Minas Gerais (UFMG) instacron:UFMG |
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UFMG |
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