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Control de modo deslizante adaptativo rápido no singular basado en la función de aproximación de la red neuronal de

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Este estudio introduce un nuevo control adaptativo basado en redes neuronales para las articulaciones del robot, mejorando la precisión y el rechazo de perturbaciones. El control de torsión rápida no singular mejorado garantiza un rendimiento robusto en aplicaciones robóticas complejas.

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Ganancia de adaptaciónNo singularRed neuronal de función de base radialMódulo de articulación del robotControl de modo deslizante de súper torsión

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Área de la Ciencia:

  • La robótica
  • Ingeniería de sistemas de control
  • Inteligencia artificial

Sus antecedentes:

  • Los módulos de articulación robótica requieren un control preciso para tareas complejas.
  • La fricción no lineal y la rigidez presentan desafíos significativos en el control robótico.
  • Los esquemas de control existentes a menudo luchan con la singularidad y las perturbaciones externas.

Objetivo del estudio:

  • Desarrollar un esquema mejorado de control de supertorsión rápida no singular para módulos de articulación de robots.
  • Para mejorar la precisión de seguimiento de la trayectoria y las capacidades de rechazo de perturbaciones.
  • Para abordar problemas de control preciso en sistemas robóticos utilizando compensación basada en redes neuronales.

Principales métodos:

  • Estableció un modelo de espacio de estado de segundo orden de módulos conjuntos de robots utilizando ecuaciones de energía de Lagrange.
  • Propuso una superficie de deslizamiento terminal no singular rápida mejorada para evitar la singularidad y acelerar la convergencia.
  • Diseñó un compensador de red neuronal de función radial para factores de modelo inciertos y una ley de control de conmutación adaptativa.

Principales resultados:

  • El sistema de control propuesto demostró un rendimiento superior de seguimiento de la trayectoria en varias trayectorias de referencia.
  • Se demostraron capacidades efectivas de rechazo de perturbaciones en presencia de perturbaciones externas.
  • La simulación y los resultados experimentales validaron la eficacia y la solidez de la estrategia de control.

Conclusiones:

  • El nuevo esquema de control de súper torsión adaptativo basado en redes neuronales mejora significativamente el control preciso de los módulos de articulación del robot.
  • El método ofrece una mayor robustez frente a las incertidumbres del modelo y las perturbaciones externas.
  • La aplicabilidad práctica para la ingeniería se mejora debido al rechazo de perturbaciones adaptativas sin información precisa.