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Robust Trajectory Tracking for Underactuated Quadrotors with Prescribed Performance
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-9706-8073
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-7309-8086
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-1927-1690
2022 (English)In: 2022 IEEE 61st Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 3351-3358Conference paper, Published paper (Refereed)
Abstract [en]

We propose a control protocol based on the prescribed performance control (PPC) methodology for a quadro-tor unmanned aerial vehicle (UAV). Quadrotor systems belong to the class of underactuated systems for which the original PPC methodology cannot be directly applied. We introduce the necessary design modifications to stabilize the considered system with prescribed performance. The proposed control protocol does not use any information of dynamic model parameters or exogenous disturbances. Furthermore, the stability analysis guarantees that the tracking errors remain inside of designer-specified time-varying functions, achieving prescribed performance independent from the control gains’ selection. Finally, simulation results verify the theoretical results.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. p. 3351-3358
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-325652DOI: 10.1109/CDC51059.2022.9992897ISI: 000948128102125Scopus ID: 2-s2.0-85147001541OAI: oai:DiVA.org:kth-325652DiVA, id: diva2:1749726
Conference
61st IEEE Conference on Decision and Control, CDC 2022, Cancun, Mexico, 6-9 December 2022
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Note

QC 20230412

Available from: 2023-04-11 Created: 2023-04-11 Last updated: 2023-05-03Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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  • text
  • asciidoc
  • rtf