Douthwaite, J.A., Mihaylova, L.S. and Veres, S.M. (2016) Enhancing Autonomy in VTOL Aircraft Based on Symbolic Computation Algorithms. In: Towards Autonomous Robotic Systems. 17th Annual Conference of Towards Autonomous Robotic Systems, June 26--July 1, 2016, Sheffield, UK. Lecture Notes in Computer Science, 9716 . Springer International Publishing ISBN 978-3-319-40378-6
Abstract
Research into the autonomy of small Unmanned Aerial Vehicles (UAVs), and especially on Vertical Take Off and Landing (VTOL) systems has intensified significantly in recent years. This paper develops a generic model of a VTOL UAV in symbolic form. The novelty of this work stems from the designed Model Predictive Control (MPC) algorithm based on this symbolic model. The MPC algorithm is compared with a state-of-the-art Linear Quadratic Regulator algorithm in attitude rate acquisition and its more accurate performance and robustness to noise is demonstrated. Results for the controllers designed for each of the aircraft’s angular rates are presented in response to input disturbances.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer International Publishing Switzerland 2016. This is an author produced version of a paper subsequently published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
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: | 18 May 2016 08:24 |
Last Modified: | 28 Oct 2016 02:15 |
Published Version: | http://dx.doi.org/10.1007/978-3-319-40379-3_10 |
Status: | Published |
Publisher: | Springer International Publishing |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-40379-3_10 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:99730 |