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Unbiased steady minimum-variance estimation for systems with measurement-delay and unknown inputs

Author

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  • Cui, Beibei
  • Song, Xinmin
  • Liu, Xiyu

Abstract

This paper considers the problem of simultaneously estimating the state and the unknown input for linear discrete-time systems with measurement delay. Firstly, the reorganized innovation analysis approach is applied to deal with measurement delay and the measurement delay model is converted into a measurement delay free model. A recursive filter where the estimation of the state and the input are interconnected is proposed. Then we utilize the innovation to obtain the unknown input estimator by least-squares estimation and the optimal state estimator is constructed by transforming into a standard Kalman filtering in terms of two Riccati equations with the same dimension as the state model. Further, the infinite horizon asymptotic stability of proposed filter is discussed. Finally we give a numerical example to show that our estimation approach is effective.

Suggested Citation

  • Cui, Beibei & Song, Xinmin & Liu, Xiyu, 2019. "Unbiased steady minimum-variance estimation for systems with measurement-delay and unknown inputs," Applied Mathematics and Computation, Elsevier, vol. 356(C), pages 379-391.
    • Handle: RePEc:eee:apmaco:v:356:y:2019:i:c:p:379-391
    DOI: 10.1016/j.amc.2019.03.036
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    References listed on IDEAS

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    1. Chen, Guoliang & Xia, Jianwei & Zhuang, Guangming & Zhao, Junsheng, 2018. "Improved delay-dependent stabilization for a class of networked control systems with nonlinear perturbations and two delay components," Applied Mathematics and Computation, Elsevier, vol. 316(C), pages 1-17.
    2. Song, Xinmin & Duan, Zhenhua & Park, Ju H., 2016. "Linear optimal estimation for discrete-time systems with measurement-delay and packet dropping," Applied Mathematics and Computation, Elsevier, vol. 284(C), pages 115-124.
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