IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i5p681-d352735.html
   My bibliography  Save this article

A Decentralized Framework for Parameter and State Estimation of Infiltration Processes

Author

Listed:
  • Song Bo

    (Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Jinfeng Liu

    (Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

Abstract

The Richards’ equation is widely used in the modeling soil water dynamics driven by the capillary and gravitational forces in the vadose zone. Its state and parameter estimation based on field soil moisture measurements is important and challenging for field applications of the Richards’ equation. In this work, we consider simultaneous state and parameter estimation of systems described by the three dimensional Richards’ equation with multiple types of soil. Based on a study on the interaction between subsystems, we propose to use decentralized estimation schemes to reduce the complexity of the estimation problem. Guidelines for subsystem decomposition are discussed and a decentralized estimation scheme developed in the framework of moving horizon state estimation is proposed. Extensive simulation results are presented to show the performance of the proposed decentralized approach.

Suggested Citation

  • Song Bo & Jinfeng Liu, 2020. "A Decentralized Framework for Parameter and State Estimation of Infiltration Processes," Mathematics, MDPI, vol. 8(5), pages 1-21, May.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:5:p:681-:d:352735
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/5/681/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/5/681/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhengzhong Yuan & Chen Zhao & Zengru Di & Wen-Xu Wang & Ying-Cheng Lai, 2013. "Exact controllability of complex networks," Nature Communications, Nature, vol. 4(1), pages 1-9, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Hyeonchae & Jung, Woo-Sung, 2016. "Structural efficiency to manipulate public research institution networks," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 21-32.
    2. Tao Jia & Robert F Spivey & Boleslaw Szymanski & Gyorgy Korniss, 2015. "An Analysis of the Matching Hypothesis in Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-12, June.
    3. Song Bo & Soumya R. Sahoo & Xunyuan Yin & Jinfeng Liu & Sirish L. Shah, 2020. "Parameter and State Estimation of One-Dimensional Infiltration Processes: A Simultaneous Approach," Mathematics, MDPI, vol. 8(1), pages 1-22, January.
    4. Xian Xi & Xiangyun Gao & Xiaotian Sun & Huiling Zheng & Congcong Wu, 2024. "Dynamic analysis and application of network structure control in risk conduction in the industrial chain," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    5. Hu, Ying & Yu, Yang & Mardani, Abbas, 2021. "Selection of carbon emissions control industries in China: An approach based on complex networks control perspective," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    6. Li, Sheng & Liu, Wenwen & Wu, Ruizi & Li, Junli, 2023. "An adaptive attack model to network controllability," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    7. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Shi, Congling, 2016. "Connectivity reliability and topological controllability of infrastructure networks: A comparative assessment," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 24-33.
    8. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Berryhill, Benjamin & Yazdani, Alireza, 2016. "Characterizing the topological and controllability features of U.S. power transmission networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 84-98.
    9. Ding, Jie & Wen, Changyun & Li, Guoqi, 2017. "Key node selection in minimum-cost control of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 251-261.
    10. Fu, Chenbo & Wang, Jinbao & Xiang, Yun & Wu, Zhefu & Yu, Li & Xuan, Qi, 2017. "Pinning control of clustered complex networks with different size," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 184-192.
    11. Li, Meizhu & Zhang, Qi & Deng, Yong, 2018. "Evidential identification of influential nodes in network of networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 283-296.
    12. Wei, Liang & Li, Faxu & Zhao, Haixing & Deng, Bo, 2024. "On the minimum driver node set of k-uniform linear hypertree networks," Applied Mathematics and Computation, Elsevier, vol. 474(C).
    13. Li, Xin-Feng & Lu, Zhe-Ming, 2016. "Optimizing the controllability of arbitrary networks with genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 422-433.
    14. Liu, Suling & Xu, Qiong & Chen, Aimin & Wang, Pei, 2020. "Structural controllability of dynamic transcriptional regulatory networks for Saccharomyces cerevisiae," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    15. Nie, Sen & Wang, Xuwen & Wang, Binghong, 2015. "Effect of degree correlation on exact controllability of multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 98-102.
    16. Yang, Qing-Lin & Wang, Li-Fu & Zhao, Guo-Tao & Guo, Ge, 2020. "A coarse graining algorithm based on m-order degree in complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    17. Cai, Ning & Cao, Jun-Wei & Khan, M. Junaid, 2015. "Almost decouplability of any directed weighted network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 637-645.
    18. Pang, Shaopeng & Hao, Fei, 2017. "Optimizing controllability of edge dynamics in complex networks by perturbing network structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 217-227.
    19. Giudici, Paolo & Huang, Bihong & Spelta, Alessandro, 2018. "Trade Networks and Economic Fluctuations in Asia," ADBI Working Papers 832, Asian Development Bank Institute.
    20. Rinaldi, Marco, 2018. "Controllability of transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 381-406.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:8:y:2020:i:5:p:681-:d:352735. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.