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    Este estudio presenta un novedoso método basado en datos para que los sistemas multiagente (SMA) logren el consenso sin necesidad de un modelo del sistema. El método de control iterativo distribuido directo (DirDILC) simplifica el análisis y avanza en la teoría de control de consenso.

    Palabras clave:
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    Área de la Ciencia:

    • Ingeniería de Control
    • Inteligencia Artificial
    • Ciencia de Sistemas

    Sus antecedentes:

    • Los sistemas multiagente (SMA) a menudo requieren modelos complejos para el control de consenso.
    • Los métodos existentes para lograr el consenso en SMA pueden ser computacionalmente intensivos y dependen de la dinámica explícita del sistema.
    • El desarrollo de enfoques libres de modelos es crucial para mejorar la adaptabilidad y aplicabilidad de los SMA.

    Objetivo del estudio:

    • Desarrollar un protocolo de control de aprendizaje directo, libre de modelos y novedoso para lograr el consenso en SMA.
    • Formular las dinámicas de consenso utilizando una función de promedio móvil autorregresivo no lineal (NARMA).
    • Establecer un marco basado en datos para analizar y diseñar protocolos de control de consenso.

    Principales métodos:

    • Se diseñó una función de promedio móvil autorregresivo no lineal (NARMA) para representar las dinámicas de consenso.
    • Se construyó un modelo de datos lineal iterativo relacionado con el rendimiento del consenso (CPiLDM) para una reformulación basada en datos.
    • Se desarrolló un método de control iterativo distribuido directo (DirDILC) utilizando optimización basada en el CPiLDM.
    • Se demostró la convergencia directamente para un sistema virtual de consenso NARMA, independientemente de la dinámica del agente.

    Principales resultados:

    • Se formularon con éxito las dinámicas de consenso utilizando NARMA y CPiLDM, lo que permite un enfoque libre de modelos.
    • El método DirDILC desarrollado logra el consenso sin depender de modelos explícitos de agentes o identificación del sistema.
    • El análisis de convergencia se simplificó al centrarse en el sistema virtual de consenso NARMA.
    • Se demostró una estrategia de control puramente basada en datos para el consenso de SMA.

    Conclusiones:

    • El método DirDILC ofrece un avance significativo en el control de consenso para SMA al eliminar la necesidad de modelos explícitos.
    • Este enfoque libre de modelos y basado en datos simplifica el análisis y mejora la aplicación práctica del control de consenso.
    • El estudio allana el camino para estrategias de coordinación multiagente más adaptables y eficientes.