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Control Óptimo Inverso en ConjuncióN Con Aprendizaje por Refuerzo Inverso para Sistemas de Parámetros Distribuidos

Xiaona Song, Zenglong Peng, Choon Ki Ahn

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    Este estudio presenta el control óptimo inverso (IOC) utilizando el aprendizaje por refuerzo inverso (IRL) para sistemas con parámetros desconocidos. El aprendizaje del comportamiento humano transfiere estrategias óptimas, mejorando el rendimiento del control en aplicaciones del mundo real.

    Palabras clave:
    Control óptimo inversoAprendizaje por refuerzo inversoSistemas de parámetros distribuidosAprendizaje del comportamiento humanoIngeniería de sistemas de controlInteligencia artificialAprendizaje automático

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    Área de la Ciencia:

    • Ingeniería de Sistemas de Control
    • Inteligencia Artificial
    • Aprendizaje Automático

    Sus antecedentes:

    • Las políticas de control óptimo pueden tener un rendimiento deficiente en sistemas de parámetros distribuidos (DPS) del mundo real debido al sesgo del modelo.
    • Las matrices de peso de recompensa predefinidas pueden provocar una degradación del rendimiento en los procesos de control óptimo.

    Objetivo del estudio:

    • Diseñar una estrategia de control óptimo inverso (IOC) para DPS con parámetros dinámicos desconocidos.
    • Abordar el desafío de transferir políticas de control óptimo a sistemas del mundo real utilizando el aprendizaje del comportamiento humano (HBL).
    • Superar los problemas de degradación del rendimiento causados por matrices de peso de recompensa fijas.

    Principales métodos:

    • Se utilizó el aprendizaje del comportamiento humano (HBL) para transferir estrategias óptimas de sistemas de referencia a DPS del mundo real.
    • Se empleó el algoritmo de iteración de políticas de aprendizaje por refuerzo inverso (IRL) para IOC en sistemas de referencia.
    • Se resolvieron las matrices de peso de recompensa equivalentes y las ganancias de control óptimo de los sistemas de referencia.

    Principales resultados:

    • Se transfirieron con éxito estrategias de control óptimo a DPS con parámetros desconocidos.
    • Se desarrolló un método para derivar matrices de peso de recompensa y ganancias de control a través de IRL.
    • Se demostró la efectividad y superioridad de los algoritmos propuestos a través de simulaciones.

    Conclusiones:

    • El diseño IOC propuesto maneja eficazmente parámetros dinámicos desconocidos en DPS.
    • HBL permite la transferencia robusta de políticas de control óptimo, mitigando el sesgo del modelo.
    • El enfoque basado en IRL determina con éxito las funciones de recompensa y las ganancias de control, mejorando el rendimiento del sistema.