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ArduPilot-Based Adaptive Autopilot: Architecture and Software-in-the-Loop Experiments.

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Title: ArduPilot-Based Adaptive Autopilot: Architecture and Software-in-the-Loop Experiments.
Authors: Baldi, Simone1 (AUTHOR) s.baldi@tudelft.nl, Sun, Danping2 (AUTHOR) sunsundp24@163.com, Xia, Xin1 (AUTHOR) xiaxin0209@gmail.com, Zhou, Guopeng3 (AUTHOR) zhgpeng@hbust.edu.cn, Liu, Di4 (AUTHOR) liud923@126.com
Source: IEEE Transactions on Aerospace & Electronic Systems. Oct2022, Vol. 58 Issue 5, p4473-4485. 13p.
Subjects: Automatic pilot (Airplanes), Adaptive control systems, Drone aircraft, Autonomous vehicles, Remotely piloted vehicles
Abstract: This article presents an adaptive method for ArduPilot-based autopilots of fixed-wing unmanned aerial vehicles (UAVs). ArduPilot is a popular open-source unmanned vehicle software suite. We explore how to augment the PID loops embedded inside ArduPilot with a model-free adaptive control method. The adaptive augmentation, adopted for both attitude and total energy control, uses input/output data without requiring an explicit model of the UAV. The augmented architecture is tested in a software-in-the-loop UAV platform in the presence of several uncertainties (unmodeled low-level dynamics, different payloads, time-varying wind, and changing mass). The performance is measured in terms of tracking errors and control efforts of the attitude and total energy control loops. Extensive experiments with the original ArduPilot, the proposed augmentation, and alternative autopilot strategies show that the augmentation can significantly improve the performance for all payloads and wind conditions: the UAV is less affected by wind and exhibits more than 70% improved tracking, with more than 7% reduced control effort. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Aerospace & Electronic Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 160621043
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PubType: Academic Journal
PubTypeId: academicJournal
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Items – Name: Title
  Label: Title
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  Data: ArduPilot-Based Adaptive Autopilot: Architecture and Software-in-the-Loop Experiments.
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  Data: <searchLink fieldCode="AR" term="%22Baldi%2C+Simone%22">Baldi, Simone</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> s.baldi@tudelft.nl</i><br /><searchLink fieldCode="AR" term="%22Sun%2C+Danping%22">Sun, Danping</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> sunsundp24@163.com</i><br /><searchLink fieldCode="AR" term="%22Xia%2C+Xin%22">Xia, Xin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xiaxin0209@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Guopeng%22">Zhou, Guopeng</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> zhgpeng@hbust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Di%22">Liu, Di</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> liud923@126.com</i>
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Aerospace+%26+Electronic+Systems%22">IEEE Transactions on Aerospace & Electronic Systems</searchLink>. Oct2022, Vol. 58 Issue 5, p4473-4485. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Automatic+pilot+%28Airplanes%29%22">Automatic pilot (Airplanes)</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Drone+aircraft%22">Drone aircraft</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Remotely+piloted+vehicles%22">Remotely piloted vehicles</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This article presents an adaptive method for ArduPilot-based autopilots of fixed-wing unmanned aerial vehicles (UAVs). ArduPilot is a popular open-source unmanned vehicle software suite. We explore how to augment the PID loops embedded inside ArduPilot with a model-free adaptive control method. The adaptive augmentation, adopted for both attitude and total energy control, uses input/output data without requiring an explicit model of the UAV. The augmented architecture is tested in a software-in-the-loop UAV platform in the presence of several uncertainties (unmodeled low-level dynamics, different payloads, time-varying wind, and changing mass). The performance is measured in terms of tracking errors and control efforts of the attitude and total energy control loops. Extensive experiments with the original ArduPilot, the proposed augmentation, and alternative autopilot strategies show that the augmentation can significantly improve the performance for all payloads and wind conditions: the UAV is less affected by wind and exhibits more than 70% improved tracking, with more than 7% reduced control effort. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IEEE Transactions on Aerospace & Electronic Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1109/TAES.2022.3162179
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 4473
    Subjects:
      – SubjectFull: Automatic pilot (Airplanes)
        Type: general
      – SubjectFull: Adaptive control systems
        Type: general
      – SubjectFull: Drone aircraft
        Type: general
      – SubjectFull: Autonomous vehicles
        Type: general
      – SubjectFull: Remotely piloted vehicles
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      – TitleFull: ArduPilot-Based Adaptive Autopilot: Architecture and Software-in-the-Loop Experiments.
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            NameFull: Baldi, Simone
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            NameFull: Xia, Xin
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              M: 10
              Text: Oct2022
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