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Extended Recursive Three-Step Filter for Linear Discrete-Time Systems with Dual-Unknown Inputs

Author

Listed:
  • Shigui Dong

    (College of Automation, Qingdao University, Qingdao 266071, China)

  • Na Wang

    (College of Automation, Qingdao University, Qingdao 266071, China
    Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao 266071, China)

  • Xueyan Wang

    (College of Automation, Qingdao University, Qingdao 266071, China)

  • Zihao Lu

    (College of Automation, Qingdao University, Qingdao 266071, China)

Abstract

This paper proposes two new extended recursive three-step filters for linear discrete systems with dual-unknown inputs, which can simultaneously estimate unknown input and state. Extended recursive three-step filter 1 (ERTSF1) introduces an innovation for obtaining the estimates of the unknown input in the measurement equation, then derives the estimates of the unknown input in the state equation. After that, it uses the already obtained estimates of the dual-unknown inputs to correct the one-step prediction of the state, and finally, it obtains the minimum-variance unbiased estimate of the system state. Extended recursive three-step filter 2 (ERTSF2) establishes a unified innovation feedback model, then applies linear minimum-variance unbiased estimation to obtain the estimates of the system state and the dual-unknown inputs to refine a more concise recursive filter. Numerical Simulation Ex-ample demonstrates the effectiveness and superiority of the two filters in this paper compared with the traditional method. The battery state of charge estimation results demonstrate the effectiveness of ERTSF2 in practical applications.

Suggested Citation

  • Shigui Dong & Na Wang & Xueyan Wang & Zihao Lu, 2023. "Extended Recursive Three-Step Filter for Linear Discrete-Time Systems with Dual-Unknown Inputs," Energies, MDPI, vol. 16(15), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5603-:d:1202185
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    References listed on IDEAS

    as
    1. Ming Zhang & Dongfang Yang & Jiaxuan Du & Hanlei Sun & Liwei Li & Licheng Wang & Kai Wang, 2023. "A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms," Energies, MDPI, vol. 16(7), pages 1-28, March.
    2. Ashikur Rahman & Xianke Lin & Chongming Wang, 2022. "Li-Ion Battery Anode State of Charge Estimation and Degradation Monitoring Using Battery Casing via Unknown Input Observer," Energies, MDPI, vol. 15(15), pages 1-19, August.
    3. Yu Hua & Na Wang & Keyou Zhao, 2021. "Simultaneous Unknown Input and State Estimation for the Linear System with a Rank-Deficient Distribution Matrix," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, January.
    4. Ming Zhang & Yanshuo Liu & Dezhi Li & Xiaoli Cui & Licheng Wang & Liwei Li & Kai Wang, 2023. "Electrochemical Impedance Spectroscopy: A New Chapter in the Fast and Accurate Estimation of the State of Health for Lithium-Ion Batteries," Energies, MDPI, vol. 16(4), pages 1-16, February.
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