Journal
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
Volume 58, Issue 5, Pages 4473-4485Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAES.2022.3162179
Keywords
Aerodynamics; Autopilot; Adaptation models; Adaptive control; Autonomous aerial vehicles; Vehicle dynamics; Control systems; ArduPilot; attitude control; autopilot; model-free adaptive control; total energy control; unmanned aerial vehicle (UAV)
Funding
- Natural Science Foundation of China [62073074]
- Special Funding for Overseas [6207011901]
- Research Fund for International Scientists [62150610499]
- Double Innovation Plan [4207012004]
- Science and Technology Plan Project of Hubei Province (second batch) [2019BEC206]
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This article presents an adaptive method for improving the performance of fixed-wing UAVs controlled by ArduPilot autopilots. The method enhances the tracking ability and reduces control effort by augmenting the PID loops with a model-free adaptive control method.
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.
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