State estimation and autonomous control of heavy-duty vehicles: a Markovian approach
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da USP |
Texto Completo: | https://doi.org/10.11606/T.18.2021.tde-27052022-100628 |
Resumo: | The past few years have seen a massive improvement in self-driving vehicle technology. However, many challenges remain ahead. For example, the autonomous control of heavyduty vehicles is still an issue because it demands robustness enough to endure huge payload variations. Also, there are still challenges concerning state estimation. For instance, take driveshaft torsion: even though it is a fundamental variable in vehicle dynamics, it is difficult to be measured or estimated due to the need for high precision encoders or because of integration estimation errors. Furthermore, gear shifting in the driveline affects state estimation and autonomous control, as it abruptly changes powertrain dynamics. Another issue is the influence of the road slope, which disturbs the system, and may or may not be measured. This thesis proposes robust discrete-time Markov jump linear system techniques for estimating driveshaft torsion and achieving autonomous driveline control. The filtering techniques are applied in two situations: with available road slope information and with unknown road slope. The algorithms are tested for a truck bodywork. Experiments show that the estimation delivers online results as accurate as offline estimation methods, especially when the road slope is known. The proposed filter is capable of estimating the torsion even in scenarios of high plant uncertainty, where an LMI-based filter only finds a highly oscillatory solution. Also, the proposed recursive controller outperforms its LMI-based counterpart in terms of tracking error and can complete the test track in scenarios where the nominal LMI-based version cannot. |
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info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis State estimation and autonomous control of heavy-duty vehicles: a Markovian approach Estimativa de estado e controle autônomo de veículos de carga: uma abordagem Markoviana 2021-08-30Marco Henrique TerraGuilherme Augusto Silva PereiraGuilherme Vianna RaffoMaíra Martins da SilvaDenis Fernando WolfLucas Barbosa MarcosUniversidade de São PauloEngenharia ElétricaUSPBR Autonomous vehicles Control Controle Filtering Filtragem Markov processes Processos de Markov Road vehicles Veículos autônomos Veículos rodoviários The past few years have seen a massive improvement in self-driving vehicle technology. However, many challenges remain ahead. For example, the autonomous control of heavyduty vehicles is still an issue because it demands robustness enough to endure huge payload variations. Also, there are still challenges concerning state estimation. For instance, take driveshaft torsion: even though it is a fundamental variable in vehicle dynamics, it is difficult to be measured or estimated due to the need for high precision encoders or because of integration estimation errors. Furthermore, gear shifting in the driveline affects state estimation and autonomous control, as it abruptly changes powertrain dynamics. Another issue is the influence of the road slope, which disturbs the system, and may or may not be measured. This thesis proposes robust discrete-time Markov jump linear system techniques for estimating driveshaft torsion and achieving autonomous driveline control. The filtering techniques are applied in two situations: with available road slope information and with unknown road slope. The algorithms are tested for a truck bodywork. Experiments show that the estimation delivers online results as accurate as offline estimation methods, especially when the road slope is known. The proposed filter is capable of estimating the torsion even in scenarios of high plant uncertainty, where an LMI-based filter only finds a highly oscillatory solution. Also, the proposed recursive controller outperforms its LMI-based counterpart in terms of tracking error and can complete the test track in scenarios where the nominal LMI-based version cannot. Nos últimos anos, houve uma melhoria significativa na tecnologia de veículos autônomos. No entanto, muitos desafios permanecem pela frente. Por exemplo, o controle autônomo de veículos pesados ainda é um problema, porque exige robustez suficiente para suportar enormes variações da carga transportada. Além disso, ainda existem desafios quanto à estimativa de estado. Por exemplo, quanto à torção nos semieixos: mesmo sendo uma variável fundamental na dinâmica do veículo, é difícil de ser medida ou estimada devido à necessidade de encoders de alta precisão ou devido a erros em estimativas por integração. Além disso, a mudança de marchas do veículo afeta a estimativa de estado e o controle autônomo, uma vez que altera de forma abrupta a dinâmica da cadeia cinemática. Outra questão é a influência do declive da estrada, que perturba o sistema, e pode ou não ser medido. Esta tese propõe técnicas robustas de sistemas lineares discretos sujeitos a saltos Markovianos para estimar a torção nos semieixos e obter o controle longitudinal autônomo do veículo por meio de sua cadeia cinemática. As técnicas serão aplicadas em duas situações: com informações disponíveis sobre a inclinação da estrada e com a inclinação da pista desconhecida. Os algoritmos são testados em um caminhão sem caçamba. Os experimentos mostram que o estimador fornece resultados online tão precisos quanto os métodos de estimativa off-line, especialmente quando o declive da estrada é conhecido. O filtro proposto consegue estimar a torção mesmo em cenários de alta incerteza da planta, em que um filtro baseado em LMI só encontra uma solução altamente oscilatória. Além disso, o controlador recursivo proposto supera seu análogo baseado em LMI em termos de erro de rastreamento, e é capaz de completar a volta de testes em cenários nos quais a versão nominal baseada em LMI é incapaz. https://doi.org/10.11606/T.18.2021.tde-27052022-100628info:eu-repo/semantics/openAccessengreponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USP2023-12-21T20:16:53Zoai:teses.usp.br:tde-27052022-100628Biblioteca 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:27212023-12-22T13:23:21.786319Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false |
dc.title.en.fl_str_mv |
State estimation and autonomous control of heavy-duty vehicles: a Markovian approach |
dc.title.alternative.pt.fl_str_mv |
Estimativa de estado e controle autônomo de veículos de carga: uma abordagem Markoviana |
title |
State estimation and autonomous control of heavy-duty vehicles: a Markovian approach |
spellingShingle |
State estimation and autonomous control of heavy-duty vehicles: a Markovian approach Lucas Barbosa Marcos |
title_short |
State estimation and autonomous control of heavy-duty vehicles: a Markovian approach |
title_full |
State estimation and autonomous control of heavy-duty vehicles: a Markovian approach |
title_fullStr |
State estimation and autonomous control of heavy-duty vehicles: a Markovian approach |
title_full_unstemmed |
State estimation and autonomous control of heavy-duty vehicles: a Markovian approach |
title_sort |
State estimation and autonomous control of heavy-duty vehicles: a Markovian approach |
author |
Lucas Barbosa Marcos |
author_facet |
Lucas Barbosa Marcos |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Marco Henrique Terra |
dc.contributor.referee1.fl_str_mv |
Guilherme Augusto Silva Pereira |
dc.contributor.referee2.fl_str_mv |
Guilherme Vianna Raffo |
dc.contributor.referee3.fl_str_mv |
Maíra Martins da Silva |
dc.contributor.referee4.fl_str_mv |
Denis Fernando Wolf |
dc.contributor.author.fl_str_mv |
Lucas Barbosa Marcos |
contributor_str_mv |
Marco Henrique Terra Guilherme Augusto Silva Pereira Guilherme Vianna Raffo Maíra Martins da Silva Denis Fernando Wolf |
description |
The past few years have seen a massive improvement in self-driving vehicle technology. However, many challenges remain ahead. For example, the autonomous control of heavyduty vehicles is still an issue because it demands robustness enough to endure huge payload variations. Also, there are still challenges concerning state estimation. For instance, take driveshaft torsion: even though it is a fundamental variable in vehicle dynamics, it is difficult to be measured or estimated due to the need for high precision encoders or because of integration estimation errors. Furthermore, gear shifting in the driveline affects state estimation and autonomous control, as it abruptly changes powertrain dynamics. Another issue is the influence of the road slope, which disturbs the system, and may or may not be measured. This thesis proposes robust discrete-time Markov jump linear system techniques for estimating driveshaft torsion and achieving autonomous driveline control. The filtering techniques are applied in two situations: with available road slope information and with unknown road slope. The algorithms are tested for a truck bodywork. Experiments show that the estimation delivers online results as accurate as offline estimation methods, especially when the road slope is known. The proposed filter is capable of estimating the torsion even in scenarios of high plant uncertainty, where an LMI-based filter only finds a highly oscillatory solution. Also, the proposed recursive controller outperforms its LMI-based counterpart in terms of tracking error and can complete the test track in scenarios where the nominal LMI-based version cannot. |
publishDate |
2021 |
dc.date.issued.fl_str_mv |
2021-08-30 |
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://doi.org/10.11606/T.18.2021.tde-27052022-100628 |
url |
https://doi.org/10.11606/T.18.2021.tde-27052022-100628 |
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.publisher.none.fl_str_mv |
Universidade de São Paulo |
dc.publisher.program.fl_str_mv |
Engenharia Elétrica |
dc.publisher.initials.fl_str_mv |
USP |
dc.publisher.country.fl_str_mv |
BR |
publisher.none.fl_str_mv |
Universidade de São Paulo |
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 |
_version_ |
1794503088692789248 |