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Simultaneous Unknown Input and State Estimation for the Linear System with a Rank-Deficient Distribution Matrix

Author

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  • Yu Hua
  • Na Wang
  • Keyou Zhao

Abstract

The classical recursive three-step filter can be used to estimate the state and unknown input when the system is affected by unknown input, but the recursive three-step filter cannot be applied when the unknown input distribution matrix is not of full column rank. In order to solve the above problem, this paper proposes two novel filters according to the linear minimum-variance unbiased estimation criterion. Firstly, while the unknown input distribution matrix in the output equation is not of full column rank, a novel recursive three-step filter with direct feedthrough was proposed. Then, a novel recursive three-step filter was developed when the unknown input distribution matrix in the system equation is not of full column rank. Finally, the specific recursive steps of the corresponding filters are summarized. And the simulation results show that the proposed filters can effectively estimate the system state and unknown input.

Suggested Citation

  • 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.
    • Handle: RePEc:hin:jnlmpe:6693690
    DOI: 10.1155/2021/6693690
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    Cited by:

    1. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    2. 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.
    3. Mohamed Hassan & Manwinder Singh & Khalid Hamid & Rashid Saeed & Maha Abdelhaq & Raed Alsaqour, 2022. "Modeling of NOMA-MIMO-Based Power Domain for 5G Network under Selective Rayleigh Fading Channels," Energies, MDPI, vol. 15(15), pages 1-19, August.
    4. Dezhi Li & Dongfang Yang & Liwei Li & Licheng Wang & Kai Wang, 2022. "Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 15(18), pages 1-26, September.
    5. Khac Huan Su & Jaeyun Yim & Wonhee Kim & Youngwoo Lee, 2022. "Lyapunov-Based Controller Using Nonlinear Observer for Planar Motors," Mathematics, MDPI, vol. 10(13), pages 1-18, June.
    6. Mahendiran T. Vellingiri & Ibrahim M. Mehedi & Thangam Palaniswamy, 2022. "A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles," Mathematics, MDPI, vol. 10(2), pages 1-15, January.
    7. Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Aspassia Daskalopulu & Dimitrios Kontogiannis & Ioannis P. Panapakidis & Lefteri H. Tsoukalas, 2022. "Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting," Energies, MDPI, vol. 15(4), pages 1-14, February.
    8. Li, Dezhi & Li, Shuo & Zhang, Shubo & Sun, Jianrui & Wang, Licheng & Wang, Kai, 2022. "Aging state prediction for supercapacitors based on heuristic kalman filter optimization extreme learning machine," Energy, Elsevier, vol. 250(C).
    9. Liu, Chunli & Li, Qiang & Wang, Kai, 2021. "State-of-charge estimation and remaining useful life prediction of supercapacitors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).

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