IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v418y2022ics0096300321009279.html
   My bibliography  Save this article

Optimal distributed filtering for nonlinear saturated systems with random access protocol and missing measurements: The uncertain probabilities case

Author

Listed:
  • Hu, Jun
  • Li, Jiaxing
  • Kao, Yonggui
  • Chen, Dongyan

Abstract

The saturated-constrained distributed filtering problem is discussed for a class of discrete nonlinear systems with random access protocol (RAP) and missing measurements (MMs) under uncertain missing probabilities (UMPs). In order to prevent data congestions, the RAP is adopted to regulate the information transmission, where only one sensor is scheduled to have the communication right and transmit the information at each time step through a shared network. In addition, the phenomenon of MMs is considered, in which the UMPs are characterized by means of the nominal means and error bounds. The main purpose of this paper is to design a distributed filter under saturation constraint such that, for both RAP and MMs under UMPs, there exists an upper bound (UB) matrix of filtering error covariance and the corresponding distributed filter gain is constructed to minimize the trace of the received UB matrix by resorting to the matrix simplifying technique. Besides, the filtering algorithm performances are discussed regarding the boundedness and monotonicity of the presented distributed filtering scheme from the mathematical perspective. Finally, some comparisons are provided to illustrate the validity of the proposed time-varying distributed filtering algorithm.

Suggested Citation

  • Hu, Jun & Li, Jiaxing & Kao, Yonggui & Chen, Dongyan, 2022. "Optimal distributed filtering for nonlinear saturated systems with random access protocol and missing measurements: The uncertain probabilities case," Applied Mathematics and Computation, Elsevier, vol. 418(C).
  • Handle: RePEc:eee:apmaco:v:418:y:2022:i:c:s0096300321009279
    DOI: 10.1016/j.amc.2021.126844
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300321009279
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2021.126844?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yao, Xiuming & Lian, Yue & Park, Ju H., 2019. "Disturbance-observer-based event-triggered control for semi-Markovian jump nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 363(C), pages 1-1.
    2. Lee, Tae H. & Park, Ju H. & Jung, Hoyoul, 2018. "Network-based H∞ state estimation for neural networks using imperfect measurement," Applied Mathematics and Computation, Elsevier, vol. 316(C), pages 205-214.
    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. Wang, Jing & Liang, Kun & Huang, Xia & Wang, Zhen & Shen, Hao, 2018. "Dissipative fault-tolerant control for nonlinear singular perturbed systems with Markov jumping parameters based on slow state feedback," Applied Mathematics and Computation, Elsevier, vol. 328(C), pages 247-262.
    2. Aravinth, N. & Satheesh, T. & Sakthivel, R. & Ran, G. & Mohammadzadeh, A., 2023. "Input-output finite-time stabilization of periodic piecewise systems with multiple disturbances," Applied Mathematics and Computation, Elsevier, vol. 453(C).
    3. Jiao, Shiyu & Shen, Hao & Wei, Yunliang & Huang, Xia & Wang, Zhen, 2018. "Further results on dissipativity and stability analysis of Markov jump generalized neural networks with time-varying interval delays," Applied Mathematics and Computation, Elsevier, vol. 336(C), pages 338-350.
    4. Wang, Jing & Hu, Xiaohui & Wei, Yunliang & Wang, Zhen, 2019. "Sampled-data synchronization of semi-Markov jump complex dynamical networks subject to generalized dissipativity property," Applied Mathematics and Computation, Elsevier, vol. 346(C), pages 853-864.
    5. Tan, Guoqiang & Wang, Zhanshan & Li, Cong, 2020. "H∞ performance state estimation of delayed static neural networks based on an improved proportional-integral estimator," Applied Mathematics and Computation, Elsevier, vol. 370(C).
    6. Li, Xin & Wei, Guoliang & Ding, Derui, 2021. "Distributed resilient interval estimation for sensor networks under aperiodic denial-of-service attacks and adaptive event-triggered protocols," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    7. Li, Yueyang & Song, Xinmin & Zhang, Zhijie & Zhao, Dong & Wang, Zhonghua, 2019. "H∞ deconvolution filter design for uncertain linear discrete time-variant systems: A Krein space approach," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 131-143.
    8. Zhang, Xiaoli & Yi, Yang & Shen, Mouquan & Wang, Qin, 2022. "Event-triggered anti-disturbance tracking control for semi-Markovian jump systems with exogenous disturbances and input saturation," Applied Mathematics and Computation, Elsevier, vol. 424(C).
    9. Wang, Jun & Shi, Kaibo & Huang, Qinzhen & Zhong, Shouming & Zhang, Dian, 2018. "Stochastic switched sampled-data control for synchronization of delayed chaotic neural networks with packet dropout," Applied Mathematics and Computation, Elsevier, vol. 335(C), pages 211-230.
    10. Kaviarasan, Boomipalagan & Kwon, Oh-Min & Park, Myeong Jin & Sakthivel, Rathinasamy, 2023. "Reduced-order filtering for semi-Markovian jump systems against randomly occurring false data injection attacks," Applied Mathematics and Computation, Elsevier, vol. 444(C).
    11. Yang, Haijiao & Ye, Dan, 2020. "Time-varying formation tracking control for high-order nonlinear multi-agent systems in fixed-time framework," Applied Mathematics and Computation, Elsevier, vol. 377(C).
    12. Shi, Xuanxuan & Shen, Mouquan, 2019. "A new approach to feedback feed-forward iterative learning control with random packet dropouts," Applied Mathematics and Computation, Elsevier, vol. 348(C), pages 399-412.
    13. Zheng, Wei & Zhang, Zhiming & Sun, Fuchun & Lam, Hak Keung & Wen, Shuhuan, 2022. "Stability analysis and robust controller design for systems with mixed time-delays and stochastic nonlinearity via cone complementarity linearization," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    14. Fu, Haijing & Li, Jiahui & Han, Fei & Hou, Nan & Dong, Hongli, 2021. "Outlier-resistant bserver-based H∞ PID control under stochastic communication protocol," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    15. Zhou, Yu & Pan, Yingnan & Li, Shubo & Liang, Hongjing, 2020. "Event-triggered cooperative containment control for a class of uncertain non-identical networks," Applied Mathematics and Computation, Elsevier, vol. 375(C).
    16. Hao, Li-Ying & Yu, Ying & Li, Hui, 2019. "Fault tolerant control of UMV based on sliding mode output feedback," Applied Mathematics and Computation, Elsevier, vol. 359(C), pages 433-455.
    17. Zeng, Hong-Bing & Zhai, Zheng-Liang & He, Yong & Teo, Kok-Lay & Wang, Wei, 2020. "New insights on stability of sampled-data systems with time-delay," Applied Mathematics and Computation, Elsevier, vol. 374(C).
    18. Xiong, Jun & Chang, Xiao-Heng & Yi, Xiaojian, 2018. "Design of robust nonfragile fault detection filter for uncertain dynamic systems with quantization," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 774-788.
    19. Yu, Peng & Ma, Yuechao, 2020. "Observer-based asynchronous control for Markov jump systems," Applied Mathematics and Computation, Elsevier, vol. 377(C).
    20. Yan, Shen & Yang, Fan & Gu, Zhou, 2020. "Derivative-based event-triggered control for networked systems with quantization," Applied Mathematics and Computation, Elsevier, vol. 383(C).

    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:eee:apmaco:v:418:y:2022:i:c:s0096300321009279. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.