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Sparse identification of parafoil dynamics model for path planning

Published online by Cambridge University Press:  13 October 2025

Zhanxin Cui Open the ORCID record for Zhanxin Cui [Opens in a new window] ,
Wen Zou ,
Zhiwei Feng Open the ORCID record for Zhiwei Feng [Opens in a new window] ,
Qingbin Zhang Open the ORCID record for Qingbin Zhang [Opens in a new window]  and
Tao Yang
Zhanxin Cui
Affiliation:
Hypersonic Technology Laboratory, National University of Defense Technology, Changsha, 410073, China College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, 410073, China
Wen Zou*
Affiliation:
Hypersonic Technology Laboratory, National University of Defense Technology, Changsha, 410073, China College of Computer, Hunan University of Technology and Business, Changsha, Hunan, Changsha, 410205, China
Zhiwei Feng
Affiliation:
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, 410073, China
Qingbin Zhang
Affiliation:
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, 410073, China
Tao Yang
Affiliation:
Hypersonic Technology Laboratory, National University of Defense Technology, Changsha, 410073, China
*
Corresponding author: Wen Zou; Email: 8320763@qq.com
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Abstract

In the field of parafoil airdrop path planning, the inherent complexity and time-sensitive nature of mission requirements necessitate rapid path generation through low-order mathematical models that approximate the system’s true dynamics. This study presents a novel sparse identification framework for constructing a parafoil path planning approximate model. Leveraging high-fidelity 9-degree-of-freedom (9 DOF) dynamic simulation data as training inputs, our method identifies simple nonlinear relationships between 3D positional coordinates (for spatial targeting) and yaw angle (for directional control), which are critical path planning parameters. Compared to conventional 4 DOF models, experimental validation using field airdrop data reveals that the proposed sparse model achieves enhanced predictive accuracy while maintaining computational efficiency. Quantitative analysis demonstrates reductions in root mean square error (RMSE) by approximately 12.96% (horizontal position), 54.44% (height) and 37.96% (yaw angle). The efficacy is further confirmed through successful fixed-point homing across diverse initial deployment scenarios, underscoring its potential for parafoil path planning.

Keywords

dynamic modelsmodel reductionpath planningparafoil systemsparse identification

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Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 21
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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