State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors

Zhang, Xiao and Ding, Feng and Yang, Erfu (2019) State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors. International Journal of Adaptive Control and Signal Processing, 33 (7). pp. 1157-1173. ISSN 0890-6327

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    Abstract

    This paper considers the state estimation problem of bilinear systems in the presence of disturbances. The standard Kalman filter is recognized as the best state estimator for linear systems, but it is not applicable for bilinear systems. It is well known that the extended Kalman filter (EKF) is proposed based on the Taylor expansion to linearize the nonlinear model. In this paper, we show that the EKF method is not suitable for bilinear systems because the linearization method for bilinear systems cannot describe the behavior of the considered system. Therefore, this paper proposes a state filtering method for the single-input–single-output bilinear systems by minimizing the covariance matrix of the state estimation errors. Moreover, the state estimation algorithm is extended to multiple-input–multiple-output bilinear systems. The performance analysis indicates that the state estimates can track the true states. Finally, the numerical examples illustrate the specific performance of the proposed method.

    Creators(s): Zhang, Xiao, Ding, Feng and Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950;
    Item type:Article
    ID code:72107
    Keywords:bilinear state estimator, Kalman filter, signal processing, state estimation, Engineering design, Signal Processing, Control and Systems Engineering, Electrical and Electronic Engineering
    Subjects:Technology > Engineering (General). Civil engineering (General) > Engineering design
    Department:Faculty of Engineering > Design, Manufacture and Engineering Management
    Depositing user: Pure Administrator
    Date deposited:20 Apr 2020 15:46
    Last modified:12 Feb 2021 04:01
    URI:https://strathprints.strath.ac.uk/id/eprint/72107
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