dc.contributor.author | Cristofaro, Andrea | |
dc.contributor.author | Aguiar, A. P. | |
dc.contributor.author | Johansen, Tor Arne | |
dc.date.accessioned | 2017-12-18T08:00:10Z | |
dc.date.available | 2017-12-18T08:00:10Z | |
dc.date.created | 2017-12-16T21:30:23Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 0890-6327 | |
dc.identifier.uri | http://hdl.handle.net/11250/2472273 | |
dc.description.abstract | A multiple-model approach for icing diagnosis and identification in small unmanned aerial vehicles is proposed. The accretion of ice layers on wings and control surfaces modifies the shape of the aircraft and, consequently, alters the performance and controllability of the vehicle. Pitot tubes might be blocked due to icing, providing errors in the airspeed measurements. In this paper, we propose a nested multiple-model adaptive estimation framework to detect and estimate icing using standard sensors only, ie, a pitot tube and an inertial measurement unit. The architecture of the estimation scheme is based on 2 different time scales, ie, one for the accretion of ice on aircraft surfaces and one for the accretion of ice on sensors, and consists of 2 nested adaptive observers, namely, outer and inner loops, respectively. The case study of a typical small unmanned aerial vehicle supports and validates the proposed theoretical results. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Wiley | nb_NO |
dc.title | Icing Detection and Identification for Unmanned Aerial Vehicles using Adaptive Nested Multiple Models | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.journal | International Journal of Adaptive Control and Signal Processing | nb_NO |
dc.identifier.doi | 10.1002/acs.2787 | |
dc.identifier.cristin | 1528428 | |
dc.relation.project | Norges forskningsråd: 223254 | nb_NO |
dc.description.localcode | This is the peer reviewed version of the following article: Cristofaro A, Johansen TA, Aguiar AP. Icing detection and identification for unmanned aerial vehicles using adaptive nested multiple models. Int J Adapt Control Signal Process. 2017;31:1584–1607 , which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/acs.2787/abstract . This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Locked until 24 May 2018 due to copyright restrictions | nb_NO |
cristin.unitcode | 194,63,25,0 | |
cristin.unitname | Institutt for teknisk kybernetikk | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |