Video Experimental Relacionado

Updated: Jan 13, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Control de seguimiento de manipuladores industriales basado en redes neuronales adaptativas RBF con aproximación de

Guirong Han^1,2, Cai Huang¹, Wei Xiao¹

¹School of Mechanical & Electrical Engineering, Wuhan Institute of Technology, Wuhan, 430205, Hubei, China.

Scientific reports

|January 9, 2026

Resumen

Este estudio presenta un nuevo algoritmo de control adaptativo para robots industriales, que mejora la precisión y la adaptabilidad sin necesidad de un modelo exacto. El método garantiza la estabilidad y compensa eficazmente las incertidumbres del sistema utilizando redes neuronales RBF y optimización por enjambre de partículas.

Sus antecedentes:

Los robots industriales requieren un control preciso para tareas complejas.
Los métodos de control existentes a menudo dependen de modelos matemáticos precisos, que son difíciles de obtener para sistemas del mundo real.
Las no linealidades e incertidumbres del sistema plantean desafíos significativos para lograr un seguimiento de trayectoria de alta precisión.

Conclusiones:

El algoritmo de control adaptativo propuesto con redes neuronales RBF es eficaz para el seguimiento de trayectoria de robots industriales.
El algoritmo compensa con éxito las incertidumbres y no linealidades del sistema en tiempo real.
El método ofrece una solución robusta, estable y adaptable para el control de robots de alta precisión.

Palabras clave:

Control adaptativo con redes neuronales RBF Robot industrial Aproximación de modelo local Estabilidad de Lyapunov Optimización PSO

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