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LPV system identification with multiple-model approach based on shifted asymmetric laplace distribution

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  • Miao Yu
  • Xianqiang Yang
  • Xinpeng Liu

Abstract

The robust linear parameters varying systems identification method with multiple-model approach is addressed in this paper. Various noise and outliers commonly exist in practical industrial processes and have a serious impact on data-driven system identification methods. A statistic approach is proposed in the paper where the centralised asymmetric Laplace (CAL) distribution is employed to model the noise and therefore the parameters estimation algorithm based on CAL distribution is robust to the symmetric/asymmetric noise and outliers. CAL distribution is represented as the product of exponential distribution and Gaussian distribution, and therefore the parameters estimation formulas are deduced in the expectation maximisation algorithm framework. The efficacy and the robustness of the proposed algorithm are verified by a numerical example and the continuous stirred tank reactor system experiment.

Suggested Citation

  • Miao Yu & Xianqiang Yang & Xinpeng Liu, 2021. "LPV system identification with multiple-model approach based on shifted asymmetric laplace distribution," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(7), pages 1452-1465, May.
    • Handle: RePEc:taf:tsysxx:v:52:y:2021:i:7:p:1452-1465
    DOI: 10.1080/00207721.2020.1859158
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    Cited by:

    1. Ziyu Gu & Shuwei Pang & Wenxiang Zhou & Yuchen Li & Qiuhong Li, 2022. "An Online Data-Driven LPV Modeling Method for Turbo-Shaft Engines," Energies, MDPI, vol. 15(4), pages 1-19, February.

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