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Related Experiment Videos

Distributed Optimization for a Class of Nonlinear Multiagent Systems With Disturbance Rejection.

Xinghu Wang, Yiguang Hong, Haibo Ji

    IEEE Transactions on Cybernetics
    |August 29, 2015
    PubMed
    Summary

    This study addresses distributed optimization in nonlinear multiagent systems facing disturbances. The proposed controller achieves optimal consensus and rejects external disturbances using local information.

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    Area of Science:

    • Control Systems Engineering
    • Optimization Theory
    • Nonlinear Systems Analysis

    Background:

    • Multiagent systems require coordinated behavior for optimal performance.
    • External disturbances can degrade system performance and stability.
    • Distributed optimization enables agents to reach consensus using only local information.

    Purpose of the Study:

    • To develop a distributed optimization controller for nonlinear multiagent systems.
    • To ensure optimal multiagent consensus despite external disturbances.
    • To reject disturbance signals modeled by exogenous systems.

    Main Methods:

    • Utilizing convex analysis and the internal model approach.
    • Designing a distributed optimization controller for heterogeneous, nonlinear agents.
    • Focusing on continuous-time minimum-phase systems with unity relative degree.

    Main Results:

    • The proposed controller successfully achieves exact optimization.
    • The controller effectively rejects local disturbance signals.
    • The design is proven to be robust in the presence of disturbances.

    Conclusions:

    • A novel distributed optimization controller is presented for nonlinear multiagent systems.
    • The controller guarantees optimal consensus and disturbance rejection.
    • The findings are applicable to complex, real-world multiagent coordination problems.