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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]
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Database: Engineering Source
Description
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]
ISSN:00189251
DOI:10.1109/TAES.2022.3162179