Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/92783
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dc.contributorDepartment of Aeronautical and Aviation Engineeringen_US
dc.creatorLu, Pen_US
dc.creatorSandy, Ten_US
dc.creatorBuchli, Jen_US
dc.date.accessioned2022-05-16T09:07:44Z-
dc.date.available2022-05-16T09:07:44Z-
dc.identifier.isbn978-1-7281-4004-9 (Electronic ISBN)en_US
dc.identifier.isbn978-1-7281-4003-2 (USB ISBN)en_US
dc.identifier.isbn978-1-7281-4005-6 (Print on Demand(PoD) ISBN)en_US
dc.identifier.urihttp://hdl.handle.net/10397/92783-
dc.descriptionIEEE/RSJ International Conference on Intelligent Robots and Systems [IROS]en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2019 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.en_US
dc.rightsThe following publication Lu, P., Sandy, T., & Buchli, J. (2019, November). Adaptive unscented Kalman filter-based disturbance rejection with application to high precision hydraulic robotic control. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4365-4372). IEEE is available at https://doi.org/10.1109/IROS40897.2019.8970476en_US
dc.titleAdaptive Unscented Kalman Filter-based disturbance rejection with application to high precision hydraulic robotic controlen_US
dc.typeConference Paperen_US
dc.identifier.spage4365en_US
dc.identifier.epage4372en_US
dc.identifier.doi10.1109/IROS40897.2019.8970476en_US
dcterms.abstractThis paper presents a novel nonlinear disturbance rejection approach for high precision model-based control of hydraulic robots. While most disturbance rejection approaches make use of observers, we propose a novel adaptive Unscented Kalman Filter to estimate the disturbances in an unbiased minimum-variance sense. The filter is made adaptive such that there is no need to tune the covariance matrix for the disturbance estimation. Furthermore, whereas most model-based control approaches require the linearization of the system dynamics, our method is nonlinear which means that no linearization is required. Through extensive simulations as well as real hardware experiments, we demonstrate that our proposed approach can achieve high precision tracking and can be readily applied to most robotic systems even in the presence of uncertainties and external disturbances. The proposed approach is also compared to existing approaches which demonstrates its superior tracking performance.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE International Conference on Intelligent Robots and Systems, 3-8 Nov. 2019, Macau, China, p. 4365-4372en_US
dcterms.issued2019-
dc.identifier.scopus2-s2.0-85081156924-
dc.description.validate202205 bckwen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberAAE-0106-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS26474442-
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