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<p>Tethered-net capture of space tumbling debris involves complex deployment and collision processes. The ejection parameters and target characteristics are critical mission parameters in the contact process. To better assess the effects of these factors, a novel time-series predictive framework is proposed. First, an accurate dynamic model of tethered-net capture is established using the finite element method (FEM). Next, a tumbling target capture device is designed to experimentally validate the established dynamic model. Finally, a predictive model is constructed to predict the entire net contact process. It is proposed using a bidirectional long short-term memory (BiLSTM) neural network and optimized by the Bayesian optimization method. The results show that the predictive model exhibits strong generalization and high predictive accuracy, achieving 96% accuracy for the entire capture process and 98% accuracy during net deployment. Additionally, the sensitivity analysis indicates that the ejection speed and target tumbling rate are the key parameters during the contact process. This work provides valuable guidance for the design and control of future active debris removal (ADR) missions.</p>

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Time-series modeling and prediction of tethered-net contact dynamics for space tumbling debris

  • 期刊论文

  • 发表日期：

    2026年5月6日

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  • Ju He,
  • Weiliang Zhu,
  • Tao Hu,
  • Zhaojun Pang,
  • Zhonghua Du

摘要

Tethered-net capture of space tumbling debris involves complex deployment and collision processes. The ejection parameters and target characteristics are critical mission parameters in the contact process. To better assess the effects of these factors, a novel time-series predictive framework is proposed. First, an accurate dynamic model of tethered-net capture is established using the finite element method (FEM). Next, a tumbling target capture device is designed to experimentally validate the established dynamic model. Finally, a predictive model is constructed to predict the entire net contact process. It is proposed using a bidirectional long short-term memory (BiLSTM) neural network and optimized by the Bayesian optimization method. The results show that the predictive model exhibits strong generalization and high predictive accuracy, achieving 96% accuracy for the entire capture process and 98% accuracy during net deployment. Additionally, the sensitivity analysis indicates that the ejection speed and target tumbling rate are the key parameters during the contact process. This work provides valuable guidance for the design and control of future active debris removal (ADR) missions.