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    Area of Science:

    • Control Engineering
    • Network Science
    • Time Series Analysis

    Background:

    • Multiagent systems require reliable communication for coordinated tasks.
    • Identifying disconnected agents is crucial for maintaining system integrity and performance.
    • Existing methods may struggle with limited data or complex system dynamics.

    Purpose of the Study:

    • To develop a robust method for identifying disconnected agents in formation-control multiagent systems.
    • To design a decision rule for detecting agent disconnectedness using external estimators.
    • To analyze the theoretical performance and practical applicability of the proposed method.

    Main Methods:

    • Employing external estimators with a decision rule inspired by unit-root testing.
    • Developing a best-effort procedure for optimal decision-making.
    • Utilizing graph theory (connected components), consensus analysis, and time-series analysis for a theoretical framework.

    Main Results:

    • The miss probability of the decision rule converges to zero as the number of data samples increases.
    • The proposed decision rule and best-effort procedure demonstrate strong performance, even with small sample sizes.
    • Simulation results validate the effectiveness of the developed analytical framework.

    Conclusions:

    • The proposed method effectively identifies disconnected agents in multiagent systems.
    • The decision rule offers a reliable and statistically sound approach to detecting communication failures.
    • The findings contribute to enhancing the robustness and fault tolerance of formation-control systems.