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Updated: Jan 15, 2026

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Control Óptimo de Seguimiento de Sistemas No Lineales Inciertos Utilizando Aprendizaje por Refuerzo Simplificado

Pengju Ning, Lingjie Duan, Changchun Hua

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    Este estudio presenta un marco de aprendizaje por refuerzo (RL) simplificado que utiliza redes neuronales (NN) mínimas para sistemas no lineales de orden superior. El novedoso enfoque reduce la complejidad computacional y garantiza la estabilidad del sistema sin necesidad de excitación persistente (PE).

    Palabras clave:
    aprendizaje por refuerzosistemas no linealescontrol de seguimientoredes neuronalesestabilidad

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    Área de la Ciencia:

    • Ingeniería de Sistemas de Control
    • Inteligencia Artificial
    • Dinámica No Lineal

    Sus antecedentes:

    • El control óptimo de seguimiento para sistemas no lineales inciertos de orden superior requiere una gran carga computacional.
    • Los métodos existentes de aprendizaje por refuerzo (RL) a menudo requieren numerosas redes neuronales (NN) y diseños recursivos complejos.
    • Un problema crítico en el RL simplificado es el potencial de análisis de estabilidad de Lyapunov inválido debido a valores propios evanescentes.

    Objetivo del estudio:

    • Desarrollar un marco de aprendizaje por refuerzo (RL) simplificado con redes neuronales (NN) mínimas para sistemas no lineales inciertos de orden superior.
    • Superar la complejidad computacional y las limitaciones teóricas de las estrategias de control existentes basadas en RL.
    • Garantizar garantías de estabilidad rigurosas sin depender de las condiciones de excitación persistente (PE).

    Principales métodos:

    • Se aprovechó la teoría de sistemas de alto orden totalmente actuados (HOFA) para reformular la dinámica del sistema en una forma normal compacta.
    • Se desarrolló un diseño de controlador unificado y no recursivo que utiliza solo tres redes neuronales (NN), independientemente del orden del sistema.
    • Se introdujo una novedosa ley de actualización de pesos crítico-actor para evitar matrices de correlación problemáticas, asegurando la validez del análisis de estabilidad.

    Principales resultados:

    • El método propuesto reduce significativamente la complejidad computacional al utilizar un número fijo de redes neuronales (NN) (tres) independientemente del orden del sistema.
    • La novedosa ley de actualización de pesos garantiza rigurosamente la semiglobal uniformidad de la convergencia del sistema en bucle cerrado.
    • Los resultados de la simulación demuestran una eficacia y eficiencia computacional superiores en comparación con los métodos de control existentes.

    Conclusiones:

    • El marco de RL simplificado ofrece una solución computacionalmente eficiente y prácticamente implementable para el control óptimo de seguimiento de sistemas no lineales de orden superior.
    • El enfoque aborda con éxito las deficiencias teóricas en las estrategias de RL simplificadas existentes, proporcionando garantías de estabilidad sólidas.
    • Este trabajo allana el camino para una aplicación más amplia de técnicas avanzadas de RL en sistemas de control complejos.