A mixed-binary non-linear programming approach for the numerical solution of a family of singular optimal control problems
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/107885 |
Resumo: | This paper presents a new approach for the efficient solution of singular optimal control problems (SOCPs). A novel feature of the proposed method is that it does not require a priori knowledge of the structure of solution. At first, the SOCP is converted into a binary optimal control problem. Then, by utilising the pseudospectral method, the resulting problem is transcribed to a mixed-binary non-linear programming problem. This mixed-binary non-linear programming problem, which can be solved by well-known solvers, allows us to detect the structure of the optimal control and to compute the approximating solution. The main advantages of the present method are that: (1) without a priori information, the structure of optimal control is detected; (2) it produces good results even using a small number of collocation points; (3) the switching times can be captured accurately. These advantages are illustrated through a numerical implementation of the method on four examples. (c) 2017 Informa UK Limited, trading as Taylor & Francis Group |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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A mixed-binary non-linear programming approach for the numerical solution of a family of singular optimal control problemsThis paper presents a new approach for the efficient solution of singular optimal control problems (SOCPs). A novel feature of the proposed method is that it does not require a priori knowledge of the structure of solution. At first, the SOCP is converted into a binary optimal control problem. Then, by utilising the pseudospectral method, the resulting problem is transcribed to a mixed-binary non-linear programming problem. This mixed-binary non-linear programming problem, which can be solved by well-known solvers, allows us to detect the structure of the optimal control and to compute the approximating solution. The main advantages of the present method are that: (1) without a priori information, the structure of optimal control is detected; (2) it produces good results even using a small number of collocation points; (3) the switching times can be captured accurately. These advantages are illustrated through a numerical implementation of the method on four examples. (c) 2017 Informa UK Limited, trading as Taylor & Francis Group20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10216/107885eng0020-717910.1080/00207179.2017.1399216Maria do Rosário de PinhoZahra ForouzandehM. Shamsiinfo: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:RCAAP2023-11-29T13:50:30Zoai:repositorio-aberto.up.pt:10216/107885Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:48:59.118997Repositório 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 |
A mixed-binary non-linear programming approach for the numerical solution of a family of singular optimal control problems |
title |
A mixed-binary non-linear programming approach for the numerical solution of a family of singular optimal control problems |
spellingShingle |
A mixed-binary non-linear programming approach for the numerical solution of a family of singular optimal control problems Maria do Rosário de Pinho |
title_short |
A mixed-binary non-linear programming approach for the numerical solution of a family of singular optimal control problems |
title_full |
A mixed-binary non-linear programming approach for the numerical solution of a family of singular optimal control problems |
title_fullStr |
A mixed-binary non-linear programming approach for the numerical solution of a family of singular optimal control problems |
title_full_unstemmed |
A mixed-binary non-linear programming approach for the numerical solution of a family of singular optimal control problems |
title_sort |
A mixed-binary non-linear programming approach for the numerical solution of a family of singular optimal control problems |
author |
Maria do Rosário de Pinho |
author_facet |
Maria do Rosário de Pinho Zahra Forouzandeh M. Shamsi |
author_role |
author |
author2 |
Zahra Forouzandeh M. Shamsi |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Maria do Rosário de Pinho Zahra Forouzandeh M. Shamsi |
description |
This paper presents a new approach for the efficient solution of singular optimal control problems (SOCPs). A novel feature of the proposed method is that it does not require a priori knowledge of the structure of solution. At first, the SOCP is converted into a binary optimal control problem. Then, by utilising the pseudospectral method, the resulting problem is transcribed to a mixed-binary non-linear programming problem. This mixed-binary non-linear programming problem, which can be solved by well-known solvers, allows us to detect the structure of the optimal control and to compute the approximating solution. The main advantages of the present method are that: (1) without a priori information, the structure of optimal control is detected; (2) it produces good results even using a small number of collocation points; (3) the switching times can be captured accurately. These advantages are illustrated through a numerical implementation of the method on four examples. (c) 2017 Informa UK Limited, trading as Taylor & Francis Group |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2017-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/107885 |
url |
https://hdl.handle.net/10216/107885 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0020-7179 10.1080/00207179.2017.1399216 |
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.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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1799135808588873728 |