Intelligent Attitude Control of Aircraft Based on LSTM
Conference paper
Li, B, Gao, P, Li, X and Chen, D (2019). Intelligent Attitude Control of Aircraft Based on LSTM. 3rd International Conference on Artificial Intelligence Applications and Technologies. Beijing, China 01 - 03 Aug 2019 IOP Publishing. https://doi.org/10.1088/1757-899X/646/1/012013
Authors | Li, B, Gao, P, Li, X and Chen, D |
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Type | Conference paper |
Abstract | © 2019 IOP Publishing Ltd. All rights reserved. The flight attitude control is the core part of the maneuvering process in air combats. Traditional flight attitude control methods have high computational complexity, low flexibility and poor ability to learn sequential feature. This paper proposes a flight attitude control model based on long short term memory network, which utilizes its special gates structure to memorize historical information, and acquire the variation law of the attitude control variable from the time sequential data including the battlefield situation and flight parameters automatically. Moreover, the basic framework and training methods of the model are also introduced, and the influence caused by various LSTM network parameters is deeply discussed. The experiment results show that the proposed model has better prediction accuracy and convergence performance than the traditional recurrent neural network. |
Year | 2019 |
Journal | IOP Conference Series: Materials Science and Engineering |
Publisher | IOP Publishing |
Journal citation | 646 (1), pp. 012013-012013 |
ISSN | 1757-8981 |
Digital Object Identifier (DOI) | https://doi.org/10.1088/1757-899X/646/1/012013 |
Accepted author manuscript | License File Access Level Open |
Publication dates | |
17 Oct 2019 | |
Online | 17 Oct 2019 |
Publication process dates | |
Accepted | 17 Sep 2019 |
Deposited | 10 Feb 2020 |
Web address (URL) of conference proceedings | https://iopscience.iop.org/journal/1757-899X |
https://openresearch.lsbu.ac.uk/item/8903x
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Accepted author manuscript
Intelligent Attitude Control of Aircraft Based on LSTM.pdf | ||
License: CC BY 4.0 | ||
File access level: Open |
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