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An interpretable machine learning model for trajectory prediction based on nonlinear dynamics mechanism constraints: applications for HVs

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Abstract

It is challenging for hypersonic vehicles (HVs) with strong nonlinear dynamics characteristics to achieve high-precision trajectory prediction. The un-interpretability of current prediction models and the difficulty in on-orbit data acquisition, high transmission costs and low data integrity bring huge obstacles to the accuracy and reliability of online prediction results. An interpretable modeling method is proposed by the physical block modeling, attention mechanism and mechanism constraints in the training process. Moreover, the binary encoding and inertial module are introduced to further improve the prediction accuracy and efficiency. The interpretability evaluation index is designed to quantitatively evaluate the degree of coincidence between the interpretable prediction model and the mechanism formulas, which proves the credibility of prediction results. The results show that the interpretable model has a better effect on the incomplete training set in terms of accuracy and efficiency. With an 8% incomplete training set, the interpretable model reduces the mean absolute error by 62.9%. After introducing the inertial module, the mean absolute error and the root-mean-square error are reduced by 40.1% and 46.0%. The developed interpretable model not only ensures the prediction accuracy, but also reduces the dependence on the training data and provides a reliable method for high-precision trajectory prediction.

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Data availability

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 12372032), the Chinese-German Center for Research Promotion (Grant No. GZ1577) and Young Elite Scientists Sponsorship Program by China Association for Science and Technology.

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Authors and Affiliations

  1. The Key Laboratory of Power Machinery and Engineering of Education Ministry, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Dengji Zhou, Dawen Huang, Yaoxin Shen, Shixi Ma & Yulin Wang

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  1. Dengji Zhou
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  2. Dawen Huang
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  3. Yaoxin Shen
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  4. Shixi Ma
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  5. Yulin Wang
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Correspondence to Dengji Zhou.

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Zhou, D., Huang, D., Shen, Y. et al. An interpretable machine learning model for trajectory prediction based on nonlinear dynamics mechanism constraints: applications for HVs. Neural Comput & Applic 36, 4083–4100 (2024). https://doi.org/10.1007/s00521-023-09249-7

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