Optimal Trajectory Planning and Robust Tracking Using Vehicle Model Inversion
| dc.rights.license | open | en_US |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | VICTOR, Stéphane
ORCID: 0000-0002-0575-0383 IDREF: 148688942 | |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | RECEVEUR, Jean-Baptiste | |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | MELCHIOR, Pierre | |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | LANUSSE, Patrick | |
| dc.date.accessioned | 2022-07-13T12:30:12Z | |
| dc.date.available | 2022-07-13T12:30:12Z | |
| dc.date.issued | 2022-05 | |
| dc.identifier.issn | 1524-9050 | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/140479 | |
| dc.description.abstractEn | This article deals with the issue of tracking a reference optimal trajectory for an autonomous nonlinear vehicle model by designing both lateral and longitudinal robust feedback control and a suited feedforward control. In previous works, a strategy based on a human-driver field of view was used to plan an optimal trajectory reference. The optimization has been made using a Genetic Algorithm (GA), and the obtained trajectory has been injected into a Potential Field (PF) so as to be reactive to unforeseen events by using a point mass model. Here, the previously developed GA-PF planification process is integrated in a new complete global planning and tracking method and applied to a validation nonlinear vehicle model. This control tracking method is developed in two strategies: a bicycle model is used as model inversion for feedforward design and a robust control is designed as feedback control in order to take the vehicle (mass and velocity) and road (slope and adherence) parameter variations into account. A lateral nonlinear control and a longitudinal robust control are designed. Realistic autonomous car simulation results are provided on an overtaking scenario and a round-about scenario. | |
| dc.language.iso | EN | en_US |
| dc.subject.en | Autonomous vehicles | |
| dc.subject.en | trajectory planning | |
| dc.subject.en | trajectory tracking | |
| dc.subject.en | optimization | |
| dc.subject.en | bicycle model | |
| dc.subject.en | model inversion | |
| dc.subject.en | robust control | |
| dc.title.en | Optimal Trajectory Planning and Robust Tracking Using Vehicle Model Inversion | |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1109/TITS.2020.3045917 | en_US |
| dc.subject.hal | Sciences de l'ingénieur [physics]/Automatique / Robotique | en_US |
| bordeaux.journal | IEEE Transactions on Intelligent Transportation Systems | en_US |
| bordeaux.page | 1-14 | en_US |
| bordeaux.volume | 23 | |
| bordeaux.hal.laboratories | Laboratoire d’Intégration du Matériau au Système (IMS) - UMR 5218 | en_US |
| bordeaux.issue | 5 | |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | Bordeaux INP | en_US |
| bordeaux.institution | CNRS | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| bordeaux.import.source | hal | |
| hal.identifier | hal-03172431 | |
| hal.version | 1 | |
| hal.export | false | |
| workflow.import.source | hal | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=IEEE%20Transactions%20on%20Intelligent%20Transportation%20Systems&rft.date=2022-05&rft.volume=23&rft.issue=5&rft.spage=1-14&rft.epage=1-14&rft.eissn=1524-9050&rft.issn=1524-9050&rft.au=VICTOR,%20St%C3%A9phane&RECEVEUR,%20Jean-Baptiste&MELCHIOR,%20Pierre&LANUSSE,%20Patrick&rft.genre=article |
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