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Neural Computing and Applications
Published in: Neural Computing and Applications 1/2022

31-07-2021 | Original Article

Longitudinal wind field prediction based on DDPG

Authors: Zhenping Yu, Panlong Tan, Qinglin Sun, Hao Sun, Zengqiang Chen

Published in: Neural Computing and Applications | Issue 1/2022

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Abstract

Parafoil is a kind of flexible aircraft, which has strong load capacity and long flight time but is easily disturbed by wind field. In the homing stage of parafoil from a high-altitude wind field to a low-altitude wind field, the low-altitude wind field is unmeasurable, which has a bad effect on the parafoil trajectory planning. To solve this problem, longitudinal prediction of the low-altitude wind field is proposed by intelligent processing of the high-altitude wind field data estimated by the parafoil. Since spatial wind field has the characteristics of hierarchical recursion and dynamic change, a deep deterministic policy gradient prediction model with Elman neural network as the core is proposed in this paper. Finally, the prediction effect of high accuracy and low-level precision attenuation, which provide reference information for the parafoil track planning, is realized.
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Metadata
Title
Longitudinal wind field prediction based on DDPG
Authors
Zhenping Yu
Panlong Tan
Qinglin Sun
Hao Sun
Zengqiang Chen
Publication date
31-07-2021
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 1/2022
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-021-06356-1

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