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Cluster Computing

, Volume 22, Supplement 3, pp 6795–6806 | Cite as

A novel aerodynamic parameter estimation algorithm via sigma point Rauch–Tung–Striebel smoother using expectation maximization

  • Wei ZhangEmail author
  • Hongwei Wang
  • Yilei Liu
  • Junyi Zuo
  • Heping Wang
Article
  • 160 Downloads

Abstract

We consider the problem of aerodynamic parameter estimation for aircraft dynamics modeled by a state space model where the statistic information of both the process and measurement noises are missing. To deal with the missing statistics, we propose in this work a new approach in which an augmented sigma point Rauch–Tung–Striebel (RTS) Kalman smoother is integrated with the expectation maximization (EM) algorithm. We define a new state vector by combining the original states and the unknown aerodynamic parameters. In addition, we impose a Gaussian random walk model for the unknown aerodynamic parameters and then build the extended state space model for the augmented RTS Kalman smoother. The expectation terms in the EM algorithm are approximated by the sigma point rule which is also applied in the augmented RTS Kalman smoother. Moreover, the non-convex optimization problem involved in the EM is solved in analytical forms rather than in numerical approaches. A comparative study of identifying the aerodynamic parameters of the flight test platform HFB-320 shows that the proposed approach achieves a substantial performance improvement over the existing ones, especially in terms of the convergence rate.

Keywords

Aircraft system identification Parameter estimation Expectation maximization algorithm Rauch–Tung–Striebel smoother Third-degree cubature rule 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 11472222 and 61473227), the National Science Foundation of Shaanxi Province of China (Grant No. 2015JM6304), the Aviation Science Foundation of China (Grant No. 20151353018), and the Aerospace Technology Support Fund of China (Grant No. 2014-HT-XGD).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Wei Zhang
    • 1
    • 2
    Email author
  • Hongwei Wang
    • 1
    • 2
  • Yilei Liu
    • 1
    • 3
  • Junyi Zuo
    • 1
    • 2
  • Heping Wang
    • 1
  1. 1.School of AeronauticsNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Experimental Aircraft Design and Flight Testing Lab of ShaanxiXi’anChina
  3. 3.AVIC Aircraft Co., LtdXi’anPeople’s Republic of China

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