Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/88565
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Mechanical Engineering | en_US |
dc.contributor | Interdisciplinary Division of Aeronautical and Aviation Engineering | en_US |
dc.creator | Zhou, W | en_US |
dc.creator | Chen, S | en_US |
dc.creator | Chang, CW | en_US |
dc.creator | Wen, CY | en_US |
dc.creator | Chen, CK | en_US |
dc.creator | Li, B | en_US |
dc.date.accessioned | 2020-12-09T02:41:25Z | - |
dc.date.available | 2020-12-09T02:41:25Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/88565 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.rights | The following publication W. Zhou, S. Chen, C. -W. Chang, C. -Y. Wen, C. -K. Chen and B. Li, "System Identification and Control for a Tail-Sitter Unmanned Aerial Vehicle in the Cruise Flight," in IEEE Access, vol. 8, pp. 218348-218359, 2020 is available at https://dx.doi.org/10.1109/ACCESS.2020.3042316. | en_US |
dc.subject | Least square | en_US |
dc.subject | Model predictive control | en_US |
dc.subject | System identification | en_US |
dc.subject | Trust region algorithm | en_US |
dc.subject | Unmanned aerial vehicles | en_US |
dc.title | System identification and control for a tail-sitter unmanned aerial vehicle in the cruise flight | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1 | en_US |
dc.identifier.spage | 218348 | - |
dc.identifier.epage | 13 | en_US |
dc.identifier.epage | 218359 | - |
dc.identifier.volume | 8 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3042316 | en_US |
dcterms.abstract | This work presents the implementation of system identification and model predictive control for a tail-sitter unmanned aerial vehicle (UAV) in cruise flight. The mathematical model of longitudinal and lateral directions of the UAV has been derived in the state-space form for grey-box modeling. The least-square regression method is augmented with regulation and solved by applying the trust-region algorithm. Outdoor flight tests were conducted to acquire the data for system identification assisted by a signal generator module. The UAV dynamic was sufficiently excited in both longitudinal and lateral directions during the flight test. The flight data were applied to the grey box system identification, and the parameters were validated by fitting the reconstructed model to a set of flight data with a different excitation waveform. The flight controller with model predictive control was formed using the identified models for flight simulation. The results demonstrate that the system identification results are able to provide reference models for the model-based controller development of the novel-design tail-sitter UAV. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE access, 2020, v. 8, p. 218348-218359 | - |
dcterms.isPartOf | IEEE access | en_US |
dcterms.issued | 2020 | - |
dc.identifier.eissn | 2169-3536 | en_US |
dc.description.validate | 202012 bcrc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a0519-n01 | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Zhou_Tail-sitter_Unmanned_Aerial.pdf | Pre-Published version | 2.53 MB | Adobe PDF | View/Open |
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