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Related Experiment Videos

Optimal Output Regulation for Heterogeneous Multiagent Systems via Adaptive Dynamic Programming.

Huaguang Zhang, Hongjing Liang, Zhanshan Wang

    IEEE Transactions on Neural Networks and Learning Systems
    |December 2, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study presents a novel adaptive dynamic programming approach for controlling heterogeneous linear multiagent systems. The method ensures synchronized outputs and minimizes errors in partially model-free systems.

    Related Experiment Videos

    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Robotics

    Background:

    • Multiagent systems often face challenges due to nonidentical agent dynamics and external disturbances.
    • Achieving optimal output regulation in such systems requires advanced control strategies.
    • Partially model-free conditions necessitate adaptive and data-driven control techniques.

    Purpose of the Study:

    • To address the optimal output regulation problem for partially model-free heterogeneous linear multiagent systems.
    • To develop a control strategy that handles nonidentical agent dynamics and exosystem-generated disturbances.
    • To achieve synchronized outputs across all agents while minimizing control energy.

    Main Methods:

    • Adaptive dynamic programming (ADP) combined with a double compensator method.
    • Design of a distributed compensator for nonidentical agents and another for the optimal performance index.
    • Development of a novel online policy iteration algorithm to obtain optimal feedback gain matrices.
    • Utilizing a spanning tree topology for information exchange among agents.

    Main Results:

    • Distributed feedback control laws were designed to synchronize agent outputs with a reference.
    • The control strategy effectively minimizes the energy of the output error.
    • The online policy iteration algorithm successfully overcame the lack of complete dynamics knowledge.
    • Effectiveness demonstrated through two illustrative examples.

    Conclusions:

    • The proposed adaptive dynamic programming and double compensator method provides an effective solution for optimal output regulation in complex multiagent systems.
    • The approach is robust to nonidentical agent dynamics and external disturbances.
    • The developed online algorithm facilitates practical implementation in partially model-free scenarios.