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Pinning Controllability for a Boolean Network With Arbitrary Disturbance Inputs.

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    This summary is machine-generated.

    This study ensures Boolean network (BN) controllability under disturbances by strategically selecting pinning nodes and control inputs. This method guarantees system stability regardless of external influences.

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    Area of Science:

    • Control Theory
    • Network Science
    • Computer Science

    Background:

    • Boolean networks (BNs) are widely used to model complex systems.
    • Controlling BNs, especially under external disturbances, is crucial for reliable system operation.
    • Existing methods often struggle with arbitrary disturbance inputs.

    Purpose of the Study:

    • To develop a method for guaranteeing pinning controllability of Boolean networks under arbitrary disturbance inputs.
    • To provide a constructive approach for selecting pinning nodes and designing control strategies.
    • To establish necessary and sufficient conditions for controllability in disturbed BNs.

    Main Methods:

    • Deriving necessary and sufficient conditions for BN controllability with arbitrary disturbances.
    • Developing a constructive method for designing the BN transition matrix.
    • Utilizing logical matrix equations to determine control input nodes.
    • Proposing a control input sequence algorithm.

    Main Results:

    • A novel method for ensuring Boolean network controllability under arbitrary disturbance inputs.
    • Identification of optimal pinning nodes based on derived conditions.
    • A systematic approach to designing control inputs for disturbed BNs.
    • Validation of the proposed method through a practical example.

    Conclusions:

    • The proposed pinning control strategy effectively guarantees the controllability of Boolean networks facing arbitrary disturbances.
    • The developed methods provide a robust framework for designing and analyzing controlled Boolean networks.
    • This research contributes a valuable tool for ensuring the stability and predictability of complex systems modeled by BNs.