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Robust Stability Clearance of Flight Control Law Based on Global Sensitivity Analysis

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Listed:
  • Liuli Ou
  • Lei Liu
  • Shuai Dong
  • Yongji Wang

Abstract

To validate the robust stability of the flight control system of hypersonic flight vehicle, which suffers from a large number of parametrical uncertainties, a new clearance framework based on structural singular value (μ) theory and global uncertainty sensitivity analysis (SA) is proposed. In this framework, SA serves as the preprocess of uncertain model to be analysed to help engineers to determine which uncertainties affect the stability of the closed loop system more slightly. By ignoring these unimportant uncertainties, the calculation of μ can be simplified. Instead of analysing the effect of uncertainties on μ which involves solving optimal problems repeatedly, a simpler stability analysis function which represents the effect of uncertainties on closed loop poles is proposed. Based on this stability analysis function, Sobol’s method, the most widely used global SA method, is extended and applied to the new clearance framework due to its suitability for system with strong nonlinearity and input factors varying in large interval, as well as input factors subjecting to random distributions. In this method, the sensitive indices can be estimated via Monte Carlo simulation conveniently. An example is given to illustrate the efficiency of the proposed method.

Suggested Citation

  • Liuli Ou & Lei Liu & Shuai Dong & Yongji Wang, 2014. "Robust Stability Clearance of Flight Control Law Based on Global Sensitivity Analysis," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnljam:v:2014:y:2014:i:1:n:153602
    DOI: 10.1155/2014/153602
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    References listed on IDEAS

    as
    1. Jinxing Shen & Wenquan Li, 2013. "Sensitivity Analysis of Wavelet Neural Network Model for Short‐Term Traffic Volume Prediction," Journal of Applied Mathematics, John Wiley & Sons, vol. 2013(1).
    2. Jinxing Shen & Wenquan Li, 2013. "Sensitivity Analysis of Wavelet Neural Network Model for Short-Term Traffic Volume Prediction," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-10, December.
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