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Data-Driven Distributed Optimal Consensus Control for Unknown Multiagent Systems With Input-Delay.

Huaipin Zhang, Dong Yue, Chunxia Dou

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    |July 12, 2018
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    Summary
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    This study introduces a data-driven approach for optimal consensus control in unknown multiagent systems (MASs) with input delays. The method transforms delayed systems into delay-free ones, enabling effective policy learning using adaptive dynamic programming.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Robotics

    Background:

    • Multiagent systems (MASs) often face challenges with unknown dynamics and input delays.
    • Achieving optimal consensus control in such systems is crucial for coordinated behavior.

    Purpose of the Study:

    • To develop a data-driven method for optimal consensus control in unknown MASs with input delays.
    • To transform the complex problem of input-delayed MAS control into a solvable delay-free equivalent.

    Main Methods:

    • Model reduction to convert input-delayed MAS into a delay-free form.
    • Application of coupled Hamilton-Jacobi-Bellman equations and optimality principles.
    • Policy iteration algorithm with distributed asynchronous updates.
    • Data-driven adaptive dynamic programming using critic-actor neural networks.

    Main Results:

    • Successfully derived optimal consensus control policies for the transformed delay-free MAS.
    • Demonstrated the online learning capability of the proposed algorithm for coupled Hamilton-Jacobi-Bellman equations.
    • Validated the effectiveness through a simulation example.

    Conclusions:

    • The proposed data-driven method effectively addresses optimal consensus control for unknown MASs with input delays.
    • The transformation to a delay-free system simplifies control policy derivation and learning.
    • Adaptive dynamic programming with neural networks provides a robust solution for practical implementation.