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State estimation for the electro-hydraulic actuator based on particle filter with an improved resampling technique

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Listed:
  • Runxia Guo
  • Zhile Wei
  • Ye Wei

Abstract

State estimation for the electro-hydraulic actuator of civil aircraft is one of the most valuable but intractable issues. Recently, the state estimation approach based on particle filters has widely attracted attention. We pursue the benefits of the data-driven approach when physical model is deficienct, and put forward some improvements that are triggered by the shortcomings of particle filters algorithm. In order to solve the particles’ degeneracy phenomenon in particle filters, a kernel function that integrates the information of probability distribution is constructed; then, the established probability kernel function is designed to represent the probability density function of resampling and the regularization form of probability density function in Hilbert space is defined. Consequently, the probability density function of resampling is obtained by solving the support vector regression model. The novel resampling method based on support vector regression-particle filters can keep the diversity of particles as well as relieve the degeneracy phenomenon and eventually make the estimated state more realistic. The approach is simulated and applied to an electro-hydraulic actuator model. The estimation results validate the effectiveness of the proposed algorithm.

Suggested Citation

  • Runxia Guo & Zhile Wei & Ye Wei, 2020. "State estimation for the electro-hydraulic actuator based on particle filter with an improved resampling technique," Journal of Risk and Reliability, , vol. 234(1), pages 41-51, February.
    • Handle: RePEc:sae:risrel:v:234:y:2020:i:1:p:41-51
    DOI: 10.1177/1748006X19871753
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    References listed on IDEAS

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    1. Michael K Pitt & Neil Shephard, "undated". "Filtering via simulation: auxiliary particle filters," Economics Papers 1997-W13, Economics Group, Nuffield College, University of Oxford.
    2. Tao, Tao & Zio, Enrico & Zhao, Wei, 2018. "A novel support vector regression method for online reliability prediction under multi-state varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 35-49.
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