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    This study presents a fully distributed optimal control algorithm for networked Euler-Lagrange systems with unknown parameters. An event-triggered approach reduces communication costs while ensuring optimal coordinated control and consensus.

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    Area of Science:

    • Control Systems Engineering
    • Networked Systems
    • Robotics

    Background:

    • Networked Euler-Lagrange (EL) systems present challenges in distributed control due to unknown model parameters.
    • Achieving optimal coordinated control and consensus on a global cost function in a distributed manner is complex.

    Purpose of the Study:

    • To develop a fully distributed optimal coordinated control algorithm for networked EL systems with unknown parameters.
    • To minimize a global cost function, which is the sum of local costs, without requiring global information.
    • To reduce communication overhead using an event-triggered mechanism.

    Main Methods:

    • Introduction of an auxiliary reference system to estimate the optimal solution of the global cost function.
    • Development of a fully distributed optimal control algorithm that does not rely on global system information.
    • Proposal of an event-triggered distributed optimal control algorithm combined with an edge-based adaptive gain method.

    Main Results:

    • The proposed algorithms achieve consensus on the optimal solution of the global cost function.
    • The event-triggered algorithm effectively reduces communication costs while maintaining distributed optimality.
    • Numerical simulations validate the theoretical findings and the effectiveness of the proposed control strategies.

    Conclusions:

    • A novel fully distributed optimal control strategy is presented for networked EL systems with unknown parameters.
    • The event-triggered approach offers a communication-efficient solution for distributed optimization problems.
    • The developed methods are robust and validated for practical implementation in networked control systems.