Sparse online Gaussian process adaptive control of unmanned aerial vehicle with slung payload

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dc.contributor.authorKartal, Muhammed Rasit
dc.contributor.authorIgnatyev, Dmitry I.
dc.contributor.authorZolotas, Argyrios
dc.date.accessioned2024-11-27T10:43:48Z
dc.date.available2024-11-27T10:43:48Z
dc.date.freetoread2024-11-27
dc.date.issued2024-11-19
dc.date.pubOnline2024-11-19
dc.description.abstractIn the past decade, Unmanned Aerial Vehicles (UAVs) have garnered significant attention across diverse applications, including surveillance, cargo shipping, and agricultural spraying. Despite their widespread deployment, concerns about maintaining stability and safety, particularly when carrying payloads, persist. The development of such UAV platforms necessitates the implementation of robust control mechanisms to ensure stable and precise maneuvering capabilities. Numerous UAV operations require the integration of payloads, which introduces substantial stability challenges. Notably, operations involving unstable payloads such as liquid or slung payloads pose a considerable challenge in this regard, falling into the category of mismatched uncertain systems. This study focuses on establishing stability for slung payload-carrying systems. Our approach involves a combination of various algorithms: the incremental backstepping control algorithm (IBKS), integrator backstepping (IBS), Proportional–Integral–Derivative (PID), and the Sparse Online Gaussian Process (SOGP), a machine learning technique that identifies and mitigates disturbances. With a comparison of linear and nonlinear methodologies through different scenarios, an investigation for an effective solution has been performed. Implementation of the machine learning component, employing SOGP, effectively detects and counteracts disturbances. Insights are discussed within the remit of rejecting liquid sloshing disturbance.
dc.description.journalNameDrones
dc.description.sponsorshipThe authors would like to express very great appreciation to the Ministry of National Education of the Republic of Türkiye for funding this project.
dc.identifier.citationKartal MR, Ignatyev DI, Zolotas A. (2024) Sparse online Gaussian process adaptive control of unmanned aerial vehicle with slung payload. Drones, Volume 8, Issue 11, November 2024, Article number 687
dc.identifier.eissn2504-446X
dc.identifier.elementsID559285
dc.identifier.issn2504-446X
dc.identifier.issueNo11
dc.identifier.paperNo687
dc.identifier.urihttps://doi.org/10.3390/drones8110687
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23230
dc.identifier.volumeNo8
dc.languageEnglish
dc.language.isoen
dc.publisherMDPI
dc.publisher.urihttps://www.mdpi.com/2504-446X/8/11/687
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4007 Control Engineering, Mechatronics and Robotics
dc.subject40 Engineering
dc.subject4602 Artificial Intelligence
dc.subjectMachine Learning and Artificial Intelligence
dc.subject40 Engineering
dc.subject46 Information and computing sciences
dc.subjectcontrol
dc.subjectUAVs
dc.subjectincremental backstepping
dc.subjectslung payload
dc.subjectagricultural drone
dc.subjectGaussian process
dc.subjectadaptive control
dc.subjectpendulum
dc.titleSparse online Gaussian process adaptive control of unmanned aerial vehicle with slung payload
dc.typeArticle
dcterms.dateAccepted2024-11-11

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