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Adaptive Consensus Control for Nonlinear Multiagent Systems With Unknown Control Directions Using Event-Triggered

Chenliang Wang, Changyun Wen, Lei Guo

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    Summary
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    This study introduces an adaptive consensus tracking control scheme for nonlinear multiagent systems with unknown control directions. The novel approach reduces communication load and ensures system stability and accurate tracking.

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    Area of Science:

    • Control Systems Engineering
    • Robotics
    • Networked Systems

    Background:

    • Multiagent systems (MAS) often face challenges with unknown control parameters.
    • Existing consensus tracking methods require continuous communication, increasing network load.
    • Unknown and non-identical control directions in agents complicate control design.

    Purpose of the Study:

    • To develop an adaptive consensus tracking control scheme for nonlinear multiagent systems.
    • To address challenges posed by completely unknown control coefficients and directions.
    • To reduce communication burden through an event-triggering mechanism.

    Main Methods:

    • Design of novel Nussbaum functions for each agent.
    • Construction of a monotonously increasing sequence to reinforce Nussbaum function effects.
    • Implementation of an event-triggering mechanism to optimize communication instants.
    • Analysis of closed-loop signal boundedness and tracking error convergence.

    Main Results:

    • Successfully circumvented the obstacle of unknown control directions.
    • Achieved global uniform boundedness of all closed-loop signals.
    • Demonstrated convergence of tracking errors to an arbitrarily small residual set.
    • Validated the effectiveness of the proposed scheme through simulations.

    Conclusions:

    • The proposed adaptive consensus tracking control scheme is effective for nonlinear multiagent systems with unknown parameters.
    • The novel approach with Nussbaum functions and event-triggering significantly enhances control performance and communication efficiency.
    • The findings offer a robust solution for decentralized control in complex networked systems.