Wang, Z., Groß, R. orcid.org/0000-0003-1826-1375 and Zhao, S. (2022) Aerobatic tic-toc control of planar quadcopters via reinforcement learning. IEEE Robotics and Automation Letters, 7 (2). pp. 2140-2147.
Abstract
This letter studies aerobatic tic-toc control of quadcopters. Tic-toc control enables rotorcraft to fly almost in the vertical plane rather than the horizontal plane. It is one of the most challenging manoeuvrers to achieve autonomously. The problem has to our knowledge not yet been studied for quadcopters. Studying it could expand their flight envelope and improve their performance in extreme, aerobatic flight tasks. In this letter, we employ a deep deterministic gradient policy approach to train reinforcement learning (RL) controllers based on carefully designed rewards. The obtained RL controllers are shown to generate two flight modes, spin and tic-toc. We analyse the properties of these flight modes and screen out unfavourable RL controllers. The qualified RL controller is then enhanced by combining it with PID and LQR controllers which achieves better flight performance and enables the quadcopter to track a moving reference point and recover to hovering flight status. Physical simulations using Simscape are presented to verify the proposed approach.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Flight control; reinforcement learning; variable-pitch propeller quadcopter |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Mar 2022 06:52 |
Last Modified: | 13 Jan 2023 01:13 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/lra.2022.3142730 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:185244 |