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Longitudinal wind field prediction based on DDPG

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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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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61973172, 61973175 and 62003177), the key Technologies Research and Development Program of Tianjin (Grant No. 19JCZDJC32800), this project also funded by China Postdoctoral Science Foundation (Grant No. 2020M670633).

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Authors and Affiliations

  1. College of Artificial Intelligence, Nankai University, Tianjin, China

    Zhenping Yu, Panlong Tan, Qinglin Sun, Hao Sun & Zengqiang Chen

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  1. Zhenping Yu
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  2. Panlong Tan
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  3. Qinglin Sun
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  4. Hao Sun
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  5. Zengqiang Chen
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Correspondence to Panlong Tan.

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Yu, Z., Tan, P., Sun, Q. et al. Longitudinal wind field prediction based on DDPG. Neural Comput & Applic 34, 227–239 (2022). https://doi.org/10.1007/s00521-021-06356-1

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