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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Quantization-Based Adaptive Fuzzy Consensus for Multiagent Systems Under Sensor Deception Attacks: A Novel

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    This study develops secure adaptive control for nonlinear multiagent systems (MASs) facing unknown directions and deception attacks. The proposed method ensures consensus tracking and system stability despite disturbances.

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    Area of Science:

    • Control Theory
    • Nonlinear Systems
    • Multiagent Systems (MASs)

    Background:

    • Investigates adaptive leader-following consensus tracking in nonlinear MASs.
    • Addresses challenges including unknown control directions, external disturbances, and sensor deception attacks.

    Purpose of the Study:

    • To design a secure adaptive consensus tracking control scheme for nonlinear MASs.
    • To handle unknown control directions and sensor deception attacks effectively.

    Main Methods:

    • Introduced a linear state transformation for an equivalent MAS with known control directions.
    • Designed a quantization-based fuzzy state observer using fuzzy-logic systems (FLS) for state estimation.
    • Developed a novel compensation mechanism and utilized Nussbaum-type functions to manage control gains and filter errors.

    Main Results:

    • Achieved convergence of consensus tracking error to a small neighborhood of the origin.
    • Ensured all signals within the closed-loop system remain bounded.
    • Demonstrated the feasibility of the secure control scheme through simulations.

    Conclusions:

    • The proposed secure control scheme effectively addresses adaptive leader-following consensus tracking in nonlinear MASs.
    • The method provides robustness against unknown control directions, disturbances, and deception attacks.