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Quantized Fuzzy Cooperative Output Regulation for Heterogeneous Nonlinear Multiagent Systems With Directed

Shanling Dong, Lu Liu, Gang Feng

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    Summary
    This summary is machine-generated.

    This study addresses cooperative output regulation for nonlinear multiagent systems using Takagi-Sugeno fuzzy models. It ensures system synchronization despite disturbances and quantization, validated with robot arm examples.

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    Area of Science:

    • Control Systems Engineering
    • Robotics
    • Artificial Intelligence

    Background:

    • Multiagent systems present challenges in cooperative control due to heterogeneity and external disturbances.
    • Takagi-Sugeno fuzzy models are effective for representing complex nonlinear dynamics in individual agents.
    • Output regulation and synchronization are critical for coordinated multiagent system performance.

    Purpose of the Study:

    • To investigate the cooperative output regulation problem for heterogeneous nonlinear multiagent systems.
    • To design distributed controllers that ensure output synchronization under fixed and switching communication topologies.
    • To handle system uncertainties, disturbances, and data quantization effectively.

    Main Methods:

    • Modeling agent dynamics using Takagi-Sugeno fuzzy systems.
    • Developing distributed reference generators for exosystem state estimation.
    • Designing distributed fuzzy cooperative controllers for individual agents.
    • Utilizing Lyapunov stability theory to derive synchronization conditions.

    Main Results:

    • Sufficient conditions for output synchronization of the closed-loop multiagent system were derived.
    • The proposed controller design ensures stability and achieves cooperative output regulation.
    • The effectiveness of the approach was demonstrated through a simulation example involving multiple single-link robot arms.

    Conclusions:

    • The proposed distributed control strategy effectively achieves cooperative output regulation for heterogeneous nonlinear multiagent systems.
    • The method is robust to external disturbances and quantization effects.
    • The successful application to a robotic system validates the practical applicability of the developed techniques.