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A Partial-Node-Based Approach to State Estimation for Complex Networks With Sensor Saturations Under Random Access

Nan Hou, Hongli Dong, Zidong Wang

    IEEE Transactions on Neural Networks and Learning Systems
    |October 13, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study develops robust state estimators for complex networks with random uncertainties and delays, using partial node measurements under a random access protocol (RAP). The estimators ensure finite-horizon H∞ performance, enhancing network state estimation reliability.

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    Area of Science:

    • Control Systems Engineering
    • Network Science
    • Stochastic Systems

    Background:

    • Complex networks (CNs) face challenges like parameter uncertainties, multiple delays, and sensor saturations.
    • Robust state estimation is crucial for monitoring and controlling these networks.
    • Partial node measurements and random access protocols (RAP) add complexity to estimation.

    Purpose of the Study:

    • To investigate robust finite-horizon state estimation for time-varying complex networks under RAP.
    • To design state estimators utilizing partial network node measurements.
    • To ensure the estimation error dynamics meet a predefined H∞ performance requirement.

    Main Methods:

    • Utilizing stochastic analysis and matrix operations to derive sufficient conditions for estimator existence.
    • Characterizing random uncertainties and delays using sequences of random variables.
    • Employing a Markov chain to model the random access protocol (RAP) for data transmission.
    • Designing time-varying estimators solvable via recursive linear matrix inequalities.

    Main Results:

    • Sufficient conditions for the existence of robust H∞ state estimators for partial-node-based complex networks are established.
    • The proposed estimators are parameterized through the solution of recursive linear matrix inequalities.
    • The developed state estimation algorithm demonstrates effectiveness in simulation.

    Conclusions:

    • Robust finite-horizon state estimation is achievable for complex networks with partial measurements under RAP.
    • The proposed method provides a systematic approach to designing effective state estimators.
    • The findings contribute to reliable state estimation in networked systems with inherent uncertainties.