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    This study introduces an event-triggered optimal bipartite consensus control for multiagent systems (MASs). The novel algorithm saves resources while ensuring stability and improving performance in systems with unknown models and input saturation.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Robotics

    Background:

    • Investigates consensus control for second-order discrete-time multiagent systems (MASs).
    • Addresses challenges of control input saturation and unknown system models.
    • Highlights limitations of conventional time-triggering control methods.

    Purpose of the Study:

    • To develop an event-triggered optimal bipartite consensus control strategy for MASs.
    • To enhance learning efficiency and resource conservation in MASs.
    • To ensure asymptotic stability and bounded tracking errors under control constraints.

    Main Methods:

    • Defined an instant reward signal with nonquadratic functions to handle control input saturation.
    • Introduced a novel internal reinforce reward function for intrinsic learning.
    • Developed a data-driven, event-triggered internal reinforce Q-learning (IrQL) algorithm using actor-critic neural networks.

    Main Results:

    • The proposed event-triggered IrQL algorithm effectively exploits the environment while conserving computational and transmission resources.
    • Functional analysis and Lyapunov stability theory confirm bounded internal reinforce reward functions and asymptotic stability of MASs.
    • Online implementation using reinforce-critic-actor neural networks demonstrated convergence and superior performance compared to existing methods.

    Conclusions:

    • The event-triggered IrQL algorithm provides an effective solution for optimal bipartite consensus control in MASs with saturation and unknown models.
    • The approach significantly improves resource efficiency and system stability.
    • Simulation results validate the algorithm's effectiveness and performance advantages.