Rodyti trumpą aprašą

dc.contributor.authorHajjami, Lhoussain El,
dc.contributor.authorMellouli, El Mehdi,
dc.contributor.authorŽuraulis, Vidas,
dc.contributor.authorBerrada, Mohammed,
dc.date.accessioned2023-12-22T07:07:19Z
dc.date.available2023-12-22T07:07:19Z
dc.date.issued2023.
dc.identifier.issn0921-8890
dc.identifier.other(crossref_id)153823220
dc.identifier.urihttps://etalpykla.vilniustech.lt/xmlui/handle/123456789/153844
dc.description.abstractThis paper intends to harness recent advances in intelligent commands to develop a novel steering control strategy for autonomous ground vehicles (AGVs) maneuvering problems. In this research, the vehicle is embodied by a single track-model (ST) taking into consideration its lateral dynamics. Besides, it is subject to unknown disturbances and uncertainties, associated with the tire/road adhesion coefficient, which are upper bounded by a positive limit. An on-line Radial Basis-function Neural-Networks (RBNN) adaptive mechanism is designed to estimate this upper limit in the sense of Lyapunov. On this basis, a novel robust adaptive neuro-sliding mode steering angle controller (AN-SMC) is introduced. Whilst the effectiveness of the controller is impacted by its parameters, a new neural-network optimization algorithm (NNA) is employed to provide optimal values for these settings. The proposed approach is evaluated through a single lane-change-maneuver planned out by a new methodology. The steering controller revealed a powerful path-tracking via the comparisons carried out in support of the developed strategy.eng
dc.formatPDF
dc.format.extentp. 1-16.
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyINSPEC
dc.relation.isreferencedbyEngineering Index
dc.rightsPrieinamas tik institucijos intranete.
dc.source.urihttps://www.sciencedirect.com/science/article/pii/S0921889023001963?v=s5
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:179264475/datastreams/MAIN/content
dc.titleA novel robust adaptive neuro-sliding mode steering controller for autonomous ground vehicles /
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.references77
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionSidi Mohamed Ben Abdellah University
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyTransporto inžinerijos fakultetas / Faculty of Transport Engineering
dc.subject.researchfieldT 003 - Transporto inžinerija / Transport engineering
dc.subject.studydirectionE12 - Transporto inžinerija / Transport engineering
dc.subject.vgtuprioritizedfieldsTD0101 - Autonominis sausumos ir oro transportas / Autonomous land and air transport
dc.subject.ltspecializationsL106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies
dc.subject.enautonomous vehicle
dc.subject.envehicle dynamics
dc.subject.enADAS systems
dc.subject.ensteering control
dc.subject.enrobust adaptive
dc.subject.enneuro-sliding mode
dc.subject.enneural-network optimization
dcterms.sourcetitleRobotics and autonomous systems.
dc.description.volumevol. 170
dc.publisher.nameElsevier B.V.
dc.publisher.cityAmsterdam
dc.identifier.doi153823220
dc.identifier.doi1-s2.0-S0921889023001963
dc.identifier.doiS0921-8890(23)00196-3
dc.identifier.doi0
dc.identifier.doi001102915700001
dc.identifier.doi10.1016/j.robot.2023.104557
dc.identifier.elaba179264475


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