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Optimal Containment Control for Stochastic Multiagent Systems via Simplified ADP Under Secure Communication.

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    Area of Science:

    • Control Systems Engineering
    • Cybersecurity
    • Artificial Intelligence

    Background:

    • Multiagent systems (MASs) face challenges in secure communication and coordinated control.
    • Optimal Containment Control (OCC) is crucial for MASs, requiring followers to stay within a region defined by leaders.
    • Stochasticity and security concerns complicate the design of effective OCC strategies.

    Purpose of the Study:

    • To develop a novel optimal containment control (OCC) strategy for nonlinear stochastic multiagent systems (MASs) with secure communication.
    • To ensure followers converge to the convex hull of leaders while minimizing control cost.
    • To address the challenges posed by stochastic dynamics and secure information exchange.

    Main Methods:

    • A novel OCC strategy is designed incorporating an encryption/decryption mechanism for secure communication.
    • A simplified adaptive dynamic programming (ADP) framework is introduced to solve the stochastic Hamilton-Jacobi-Bellman (HJB) equation.
    • A single critic network weights tuning rule is developed using the experience replay technique (ERT).

    Main Results:

    • The proposed OCC strategy ensures followers converge to the convex hull of leaders.
    • Secure communication is maintained through encryption and decryption, preserving data integrity.
    • The adaptive dynamic programming approach with ERT effectively solves the HJB equation, relaxing traditional requirements.

    Conclusions:

    • The developed OCC strategy is effective for nonlinear stochastic multiagent systems under secure communication.
    • The integration of ADP and ERT provides a robust framework for solving complex control problems.
    • The theoretical analysis confirms the uniform ultimate boundedness of the closed-loop system, validating the approach.