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Control óptimo activado por eventos para manipuladores modulares reconfigurables con restricciones de entrada basadas

Fan Zhou1, Yifan Zhang1, Tianhao Ma2

  • 1School of Electrical and Electronic Engineering, Changchun University of Technology, 130012, Changchun, China.

ISA transactions
|September 4, 2025
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Resumen

Este estudio introduce un control óptimo desencadenado por eventos para los manipuladores reconfigurables modulares (MRM) utilizando el control predictivo del modelo (MPC). El método mejora el rendimiento y la robustez al tiempo que garantiza la seguridad a través de restricciones de par y programación dinámica adaptativa.

Palabras clave:
Programación dinámica adaptativaControl desencadenado por sucesosModelo de control predictivoManipuladores modulares reconfigurables

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Área de la Ciencia:

  • Robótica y sistemas de control
  • La inteligencia artificial en la automatización
  • Teoría de control avanzada

Sus antecedentes:

  • Los manipuladores modulares reconfigurables (MRM) presentan desafíos de control complejos debido a sus estructuras adaptables.
  • Los métodos de control existentes a menudo luchan con la coordinación descentralizada y la robustez contra las incertidumbres del modelo.
  • Garantizar la seguridad a través de restricciones de entrada es fundamental para las aplicaciones prácticas de MRM.

Objetivo del estudio:

  • Desarrollar una estrategia de control óptima para los MRM desencadenada por eventos.
  • Para mejorar el rendimiento, la robustez y la seguridad del sistema.
  • Abordar los desafíos del control descentralizado y las inexactitudes del modelo en los MRM.

Principales métodos:

  • Un enfoque descentralizado de control predictivo de modelo (MPC) descompone el control de MRM en tareas específicas de módulos coordinadas por un marco global.
  • Las funciones tangentes hiperbólicas se emplean para las restricciones de par de entrada para evitar riesgos de seguridad.
  • La programación dinámica adaptativa (ADP) está integrada con MPC para mejorar la robustez contra los errores de modelado.
  • Se utiliza una red neuronal crítica (NN) para resolver la ecuación de Hamilton-Jacobi-Bellman (HJB) para obtener soluciones de control óptimas.
  • La teoría de la estabilidad de Lyapunov se aplica para garantizar un límite final uniforme (UUB) de los errores de seguimiento de la trayectoria.

Principales resultados:

  • El método MPC desencadenado por eventos propuesto reduce significativamente los errores de seguimiento de trayectoria en los MRM.
  • El consumo de recursos se minimiza a través de la estrategia de control eficiente y descentralizada.
  • Se mejoran las capacidades de par limitado, mejorando la seguridad operativa.
  • La integración de ADP y NN demuestra una mayor solidez del sistema.

Conclusiones:

  • El método de control óptimo desencadenado por eventos desarrollado ofrece una solución robusta y eficiente para los MRM.
  • El enfoque equilibra efectivamente el rendimiento, la seguridad y la adaptabilidad en sistemas robóticos complejos.
  • Este trabajo avanza en el estado de la técnica en el control de plataformas robóticas modulares y reconfigurables.