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On extended state based Kalman filter design for a class of nonlinear time-varying uncertain systems

  • Wenyan Bai
  • Wenchao Xue
  • Yi Huang
  • Haitao Fang
Research Paper
  • 72 Downloads

Abstract

This paper considers the filtering problem for a class of multi-input multi-output systems with nonlinear time-varying uncertain dynamics, random process and measurement noise. An extended state based Kalman filter, with the idea of timely estimating the unknown dynamics, is proposed for better robustness and higher estimation precision. The stability of the proposed filter is rigorously proved for nonlinear timevarying uncertain system with weaker stability condition than the extended Kalman filter, i.e., the initial estimation error, the uncertain dynamics and the noises are only required to be bounded rather than small enough. Moreover, quantitative precision of the proposed filter is theoretically evaluated. The proposed algorithm is proved to be the asymptotic unbiased minimum variance filter for constant uncertainty. The simulation results of some benchmark examples demonstrate the feasibility and effectiveness of the method.

Keywords

Kalman filter extended state observer nonlinear time-varying uncertain system unbiased minimum variance filter active disturbance rejection control 

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Wenyan Bai
    • 1
  • Wenchao Xue
    • 2
    • 3
  • Yi Huang
    • 2
    • 3
  • Haitao Fang
    • 2
    • 3
  1. 1.Beijing Aerospace Automatic Control InstituteBeijingChina
  2. 2.LSC, NCMIS, Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina
  3. 3.School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijingChina

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