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Conference Paper: Joint unscented kalman filter for dual estimation in a bifilar pendulum for a small UAV

TitleJoint unscented kalman filter for dual estimation in a bifilar pendulum for a small UAV
Authors
KeywordsJoint Unscented Kalman Filter
Machine learning
Model identification
Dual estimation
UAV
Quadrotor
Issue Date2015
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1002417
Citation
The 10th Asian Control Conference (ASCC 2015), Kota Kinabalu, Malaysia, 31 May-3 June 2015. In Conference Proceedings, 2015, p. 1-6 How to Cite?
AbstractIt has always been difficult to accurately estimate the moment of inertia of an object, e.g. an unmanned aerial vehicle (UAV). Whilst various offline estimation methods exist to allow accurate parametric estimation by minimizing an error cost function, they require large memory consumption, high computational effort, and a long convergence time. The initial estimate's accuracy is also vital in attaining convergence. In this paper, a new real time solution to the model identification problem is provided with the use of a Joint Unscented Kalman Filter for dual estimation. The identification procedures can be easily implemented using a microcontroller, a gyroscope sensor, and a simple bifilar pendulum setup. Accuracy, robustness, and convergence speed are achieved.
Persistent Identifierhttp://hdl.handle.net/10722/217501
ISBN

 

DC FieldValueLanguage
dc.contributor.authorMa, C-
dc.contributor.authorChen, MZ-
dc.contributor.authorLam, J-
dc.contributor.authorCheung, KC-
dc.creatorsml 101516-
dc.date.accessioned2015-09-18T06:01:05Z-
dc.date.available2015-09-18T06:01:05Z-
dc.date.issued2015-
dc.identifier.citationThe 10th Asian Control Conference (ASCC 2015), Kota Kinabalu, Malaysia, 31 May-3 June 2015. In Conference Proceedings, 2015, p. 1-6-
dc.identifier.isbn978-1-4799-7862-5-
dc.identifier.urihttp://hdl.handle.net/10722/217501-
dc.description.abstractIt has always been difficult to accurately estimate the moment of inertia of an object, e.g. an unmanned aerial vehicle (UAV). Whilst various offline estimation methods exist to allow accurate parametric estimation by minimizing an error cost function, they require large memory consumption, high computational effort, and a long convergence time. The initial estimate's accuracy is also vital in attaining convergence. In this paper, a new real time solution to the model identification problem is provided with the use of a Joint Unscented Kalman Filter for dual estimation. The identification procedures can be easily implemented using a microcontroller, a gyroscope sensor, and a simple bifilar pendulum setup. Accuracy, robustness, and convergence speed are achieved.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1002417-
dc.relation.ispartofAsian Control Conference-
dc.rightsAsian Control Conference. Copyright © IEEE.-
dc.rights©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectJoint Unscented Kalman Filter-
dc.subjectMachine learning-
dc.subjectModel identification-
dc.subjectDual estimation-
dc.subjectUAV-
dc.subjectQuadrotor-
dc.titleJoint unscented kalman filter for dual estimation in a bifilar pendulum for a small UAV-
dc.typeConference_Paper-
dc.identifier.emailChen, MZ: mzqchen@hku.hk-
dc.identifier.emailLam, J: jlam@hku.hk-
dc.identifier.emailCheung, KC: kccheung@hku.hk-
dc.identifier.authorityChen, MZ=rp01317-
dc.identifier.authorityLam, J=rp00133-
dc.identifier.authorityCheung, KC=rp01322-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ASCC.2015.7244614-
dc.identifier.hkuros250977-
dc.identifier.hkuros254434-
dc.identifier.spage1-
dc.identifier.epage6-
dc.publisher.placeUnited States-

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