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Uncertainty quantification in the deployment of tensegrity

  • Haoran Zou
  • , Alessandro A. Quarta
  • , Lei Wu
  • , Luisa Boni
  • , Wenhao Li
  • , Songlin Bai
  • , Zichen Deng
  • Northwestern Polytechnical University Xian
  • University of Pisa
  • Tongji University

Research output: Contribution to journalArticlepeer-review

Abstract

This study investigates input parameter uncertainties in the deployment of a three-bar tensegrity and proposes an integrated analysis framework that combines uncertainty quantification, sensitivity assessment, and infinitesimal mechanism–based path optimization. Leveraging Latin hypercube sampling in conjunction with an accurate dynamic model, four key parameters, i.e., pretension, damping coefficient, deployment time, and initial angle, are examined for their influence on deployment performance. The method is evaluated through a simulation-based approach, and in this context, the numerical results indicate that the initial angle is the dominant factor influencing both energy consumption and path-tracking accuracy. Compared with the nominal design value, its interaction with deployment time can cause energy consumption variations of up to approximately 50% and path error deviations of about (Formula presented). The damping coefficient is the primary driver of energy consumption, while deployment time exerts a secondary influence on path error. Furthermore, several statistical and sensitivity analysis methods are used to reveal the effects of the input variables and their interactions. The results provide quantitative guidance for parameter optimization in on-orbit deployment strategies and robust control.

Original languageEnglish
JournalAdvances in Space Research
DOIs
StateAccepted/In press - 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Global sensitivity analysis
  • Latin hypercube sampling
  • On-orbit deployment
  • Tensegrity
  • Uncertainty quantification

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