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Improved Square-Root Cubature Kalman Filtering Algorithm for Nonlinear Systems with Dual Unknown Inputs

Author

Listed:
  • Zihao Lu

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

  • Shigui Dong

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

Abstract

For nonlinear discrete systems with dual unknown inputs, there are many limitations regarding previous nonlinear filters. This paper proposes two new, improved square-root cubature Kalman filtering (ISRCKF) algorithms to estimate system states and dual unknown inputs. Improved square-root cubature Kalman filtering 1 (ISRCKF1) introduces an innovation that first obtains the unknown input estimates from the measurement equation, then updates the innovation to derive the unknown input estimates from the state equation, then uses the already obtained estimates of the dual unknown inputs to correct the one-step estimate of the state, and finally the minimum variance unbiased estimate of the state is obtained. Improved square-root cubature Kalman filtering 2 (ISRCKF2) builds a unified innovation feedback model, then applies the minimum variance unbiased estimation (MVUE) criterion to obtain the estimates of system states and dual unknown inputs, refining a more concise recursive filter but requiring stronger assumptions. Finally, simulation results demonstrate that the above two algorithms can achieve the optimal estimates of system states and dual unknown inputs simultaneously, and ISRCKF2 further enhances the accuracy of both state and dual unknown inputs estimation, which verifies the validity of the proposed algorithms.

Suggested Citation

  • Zihao Lu & Na Wang & Shigui Dong, 2023. "Improved Square-Root Cubature Kalman Filtering Algorithm for Nonlinear Systems with Dual Unknown Inputs," Mathematics, MDPI, vol. 12(1), pages 1-31, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:99-:d:1308737
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    References listed on IDEAS

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    1. Wei Zhao & Huiguang Li & Liying Zou & Wenjuan Huang, 2017. "Nonlinear Unknown Input Observer Based on Singular Value Decomposition Aided Reduced Dimension Cubature Kalman Filter," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-13, March.
    2. Li, Yueyang & Liu, Shuai & Zhong, Maiying & Ding, Steven X., 2018. "State estimation for stochastic discrete-time systems with multiplicative noises and unknown inputs over fading channels," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 116-130.
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