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dc.contributor.authorTartaglione, Gaetano
dc.contributor.authorD'Amato, Egidio
dc.contributor.authorAriola, Marco
dc.contributor.authorSalvo Rossi, Pierluigi
dc.contributor.authorJohansen, Tor Arne
dc.date.accessioned2017-12-12T15:05:03Z
dc.date.available2017-12-12T15:05:03Z
dc.date.created2017-10-18T10:36:29Z
dc.date.issued2017
dc.identifier.citationRobotics and Autonomous Systems. 2017, 92 1-11.nb_NO
dc.identifier.issn0921-8890
dc.identifier.urihttp://hdl.handle.net/11250/2470898
dc.description.abstractIn this paper we present a multi-level and distributed control system, based on a robust Model Predictive Control (MPC) technique, for a multi-body slung-load system. In particular, we consider a swarm of autonomous multi-copters which are connected by wires to a suspended payload. The payload reference trajectory is obtained through a constrained optimization, then the reference trajectory for each UAV is derived on the basis of the known shape of the formation, while taking into account operational constraints such as collision avoidance and cruise speed. Trajectory tracking is performed by a multi-level flight control system based on a MPC technique and a PID control system. Numerical simulations have been performed in order to test the control system in realistic scenarios. In particular, the multi-copters are modeled by the six Degrees-of-Freedom (6DOF) model, the constraint forces on the wires are calculated using the Udwadia–Kalaba equation and the external disturbances (atmospheric turbulence and gust) are included in the simulation. Simulation results are encouraging, thus making the proposed system an appealing candidate for similar applications.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleModel predictive control for a multi-body slung-load systemnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber1-11nb_NO
dc.source.volume92nb_NO
dc.source.journalRobotics and Autonomous Systemsnb_NO
dc.identifier.doi10.1016/j.robot.2017.02.007
dc.identifier.cristin1505471
dc.relation.projectNorges forskningsråd: 223254nb_NO
dc.description.localcode© 2017. This is the authors’ accepted and refereed manuscript to the article. Locked until 24.2.2019 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/nb_NO
cristin.unitcode194,63,35,0
cristin.unitcode194,63,25,0
cristin.unitnameInstitutt for elektroniske systemer
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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